Package check result: OK Changes to worse in reverse depends: Package: adjclust Check: tests New result: ERROR Running ‘testthat.R’ [28s/28s] Running the tests in ‘tests/testthat.R’ failed. Complete output: > library("testthat") > library("adjclust") > > test_check("adjclust") Saving _problems/test_modify-19.R object has no names - using numeric order for row/column names [ FAIL 1 | WARN 1 | SKIP 3 | PASS 171 ] ══ Skipped tests (3) ═══════════════════════════════════════════════════════════ • No NA value: nothing to test here! (3): 'test_snpClust_NA-in-LD.R:22:3', 'test_snpClust_NA-in-LD.R:57:3', 'test_snpClust_NA-in-LD.R:78:3' ══ Failed tests ════════════════════════════════════════════════════════════════ ── Error ('test_modify.R:19:3'): Results of the algorithm are shifted by lambda when similarity is unnormalized and heights are positive ── Error in `expect_message(fit4 <- adjClust(sim2), fit3$correction)`: `regexp` must be a single string, `NA`, or `NULL`, not the number 1.8. Backtrace: ▆ 1. └─testthat::expect_message(regexp = fit3$correction) at test_modify.R:19:3 2. └─testthat:::check_string(regexp, allow_null = TRUE, allow_na = TRUE) 3. └─testthat:::stop_input_type(...) 4. └─rlang::abort(message, ..., call = call, arg = arg) [ FAIL 1 | WARN 1 | SKIP 3 | PASS 171 ] Error: ! Test failures. Execution halted Package: arulesCBA Check: tests New result: ERROR Running ‘testthat.R’ [18s/17s] Running the tests in ‘tests/testthat.R’ failed. Complete output: > library("testthat") > library("arulesCBA") Loading required package: Matrix Loading required package: arules Attaching package: 'arules' The following objects are masked from 'package:base': abbreviate, write > > testthat::test_check("arulesCBA") Saving _problems/test-CBA_helpers-18.R [ FAIL 1 | WARN 0 | SKIP 2 | PASS 55 ] ══ Skipped tests (2) ═══════════════════════════════════════════════════════════ • empty test (2): , ══ Failed tests ════════════════════════════════════════════════════════════════ ── Error ('test-CBA_helpers.R:18:1'): (code run outside of `test_that()`) ────── Error in `expect(length(r), length(cls))`: `ok` must be `TRUE` or `FALSE`, not the number 2. Backtrace: ▆ 1. └─testthat::expect(ok = length(r)) at test-CBA_helpers.R:18:1 2. └─testthat:::check_bool(ok) 3. └─testthat:::stop_input_type(...) 4. └─rlang::abort(message, ..., call = call, arg = arg) [ FAIL 1 | WARN 0 | SKIP 2 | PASS 55 ] Error: ! Test failures. Execution halted Package: autodb Check: tests New result: ERROR Running ‘spelling.R’ [0s/0s] Running ‘testthat.R’ [411s/413s] Running the tests in ‘tests/testthat.R’ failed. Complete output: > library(testthat) > library(autodb) Attaching package: 'autodb' The following object is masked from 'package:stats': decompose > > test_check("autodb") Saving _problems/test-database-984.R Saving _problems/test-database_schema-746.R Saving _problems/test-functional_dependency-276.R Saving _problems/test-relation-726.R Saving _problems/test-relation_schema-620.R [ FAIL 5 | WARN 0 | SKIP 1 | PASS 725 ] ══ Skipped tests (1) ═══════════════════════════════════════════════════════════ • empty test (1): 'test-decompose.r:130:3' ══ Failed tests ════════════════════════════════════════════════════════════════ ── Failure ('test-database.r:982:5'): database / concatenates without losing attribute orderings, if consistent ── Expected exactly one failure and no successes. Actually failed 2 times ── Failure ('test-database_schema.r:744:5'): database_schema / concatenates without losing attribute orderings, if consistent ── Expected exactly one failure and no successes. Actually failed 2 times ── Failure ('test-functional_dependency.r:276:5'): functional_dependency / concatenates without losing attribute orderings, if consistent ── Expected exactly one failure and no successes. Actually failed 2 times ── Failure ('test-relation.r:726:5'): relation / concatenates without losing attribute orderings, if consistent ── Expected exactly one failure and no successes. Actually failed 2 times ── Failure ('test-relation_schema.r:620:5'): relation_schema / concatenates without losing attribute orderings, if consistent ── Expected exactly one failure and no successes. Actually failed 2 times [ FAIL 5 | WARN 0 | SKIP 1 | PASS 725 ] Error: ! Test failures. Execution halted Package: aws.comprehend Check: tests New result: ERROR Running ‘testthat.R’ [2s/2s] Running the tests in ‘tests/testthat.R’ failed. Complete output: > library(testthat) > library(aws.comprehend) > > test_check("aws.comprehend") Saving _problems/test-detect_entities-10.R Saving _problems/test-detect_entities-26.R Saving _problems/test-detect_language-9.R Saving _problems/test-detect_language-24.R Saving _problems/test-detect_medical_entities-11.R Saving _problems/test-detect_medical_entities-36.R Saving _problems/test-detect_medical_phi-10.R Saving _problems/test-detect_phrases-10.R Saving _problems/test-detect_phrases-26.R Saving _problems/test-detect_sentiment-10.R Saving _problems/test-detect_sentiment-26.R Saving _problems/test-detect_syntax-10.R Saving _problems/test-detect_syntax-31.R [ FAIL 13 | WARN 0 | SKIP 0 | PASS 10 ] ══ Failed tests ════════════════════════════════════════════════════════════════ ── Error ('test-detect_entities.R:6:3'): detect_entities works on single string ── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test-detect_entities.R:6:3 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) ── Error ('test-detect_entities.R:22:3'): detect_entities works on character vector ── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test-detect_entities.R:22:3 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) ── Error ('test-detect_language.R:6:3'): detect_language works on single string ── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(comprehendHTTP = mock_comprehendHTTP, detect_language(text = body$single_language$Text)) at test-detect_language.R:6:3 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) ── Error ('test-detect_language.R:20:3'): detect_language works on character vector ── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test-detect_language.R:20:3 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) ── Error ('test-detect_medical_entities.R:6:3'): detect_medical_entities V1 works on single string ── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test-detect_medical_entities.R:6:3 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) ── Error ('test-detect_medical_entities.R:31:3'): detect_medical_entities V2 works on single string ── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test-detect_medical_entities.R:31:3 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) ── Error ('test-detect_medical_phi.R:6:3'): detect_medical_phi works on single string ── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test-detect_medical_phi.R:6:3 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) ── Error ('test-detect_phrases.R:6:3'): detect_phrases works on single string ── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test-detect_phrases.R:6:3 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) ── Error ('test-detect_phrases.R:22:3'): detect_phrases works on character vector ── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test-detect_phrases.R:22:3 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) ── Error ('test-detect_sentiment.R:6:3'): detect_sentiment works on single string ── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test-detect_sentiment.R:6:3 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) ── Error ('test-detect_sentiment.R:22:3'): detect_sentiment works on character vector ── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test-detect_sentiment.R:22:3 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) ── Error ('test-detect_syntax.R:6:3'): detect_syntax works on single string ──── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test-detect_syntax.R:6:3 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) ── Error ('test-detect_syntax.R:27:3'): detect_syntax works on character vector ── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test-detect_syntax.R:27:3 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) [ FAIL 13 | WARN 0 | SKIP 0 | PASS 10 ] Error: ! Test failures. Execution halted Package: bindr Check: tests New result: ERROR Running ‘testthat.R’ [1s/1s] Running the tests in ‘tests/testthat.R’ failed. Complete output: > library(testthat) > library(bindr) > > test_check("bindr") Saving _problems/test-error-13.R [ FAIL 1 | WARN 0 | SKIP 0 | PASS 41 ] ══ Failed tests ════════════════════════════════════════════════════════════════ ── Error ('test-error.R:9:3'): non-native encoding causes warning ────────────── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test-error.R:9:3 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) [ FAIL 1 | WARN 0 | SKIP 0 | PASS 41 ] Error: ! Test failures. Execution halted Package: BiocManager Check: tests New result: ERROR Running ‘testthat.R’ [4s/7s] Running the tests in ‘tests/testthat.R’ failed. Complete output: > library(testthat) > library(BiocManager) Bioconductor version '3.22' requires R version '4.5'; use `version = '3.23'` with R version 4.6; see https://bioconductor.org/install > > test_check("BiocManager") Saving _problems/test_version-373.R Saving _problems/test_version-385.R Saving _problems/test_version-424.R [ FAIL 3 | WARN 2 | SKIP 28 | PASS 75 ] ══ Skipped tests (28) ══════════════════════════════════════════════════════════ • On CRAN (9): 'test_repositories.R:80:5', 'test_repositories.R:173:5', 'test_repositories.R:211:5', 'test_version.R:17:5', 'test_version.R:39:5', 'test_version.R:235:5', 'test_version.R:246:5', 'test_version.R:277:5', 'test_version.R:428:5' • mis-configuration, R 4.6.0, Bioconductor 3.22 (18): 'test_install.R:26:5', 'test_install.R:44:5', 'test_install.R:218:5', 'test_install.R:347:5', 'test_repositories.R:4:5', 'test_repositories.R:13:5', 'test_repositories.R:19:5', 'test_repositories.R:25:5', 'test_repositories.R:33:5', 'test_repositories.R:89:5', 'test_repositories.R:100:5', 'test_repositories.R:163:5', 'test_valid.R:4:5', 'test_version.R:91:5', 'test_version.R:106:5', 'test_version.R:146:5', 'test_version.R:193:5', 'test_version.R:327:5' • too idiosyncratic for standardized testing (1): 'test_install.R:179:5' ══ Failed tests ════════════════════════════════════════════════════════════════ ── Error ('test_version.R:365:5'): .version_BiocVersion returns .version_sentinel output ── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test_version.R:365:5 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) ── Error ('test_version.R:377:5'): .version_map_get_offline() works ──────────── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test_version.R:377:5 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) ── Error ('test_version.R:413:5'): version chooses best ──────────────────────── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test_version.R:413:5 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) [ FAIL 3 | WARN 2 | SKIP 28 | PASS 75 ] Error: ! Test failures. Execution halted Package: clinUtils Check: tests New result: ERROR Running ‘testthat.R’ [14s/15s] Running the tests in ‘tests/testthat.R’ failed. Complete output: > library(testthat) > library(clinUtils) > > test_check("clinUtils") Saving _problems/test_getClinDT-139.R Saving _problems/test_getClinDT-176.R [ FAIL 2 | WARN 1 | SKIP 2 | PASS 305 ] ══ Skipped tests (2) ═══════════════════════════════════════════════════════════ • On CRAN (2): 'test_getClinDT.R:838:7', 'test_getClinDT.R:855:7' ══ Failed tests ════════════════════════════════════════════════════════════════ ── Error ('test_getClinDT.R:139:7'): Invisible columns are not shown in the DataTables output ── Error in `expect(cDefs[[which(cDefsNonVisible)]]$targets, 1)`: `ok` must be `TRUE` or `FALSE`, not the number 1. Backtrace: ▆ 1. └─testthat::expect(ok = cDefs[[which(cDefsNonVisible)]]$targets) at test_getClinDT.R:139:7 2. └─testthat:::check_bool(ok) 3. └─testthat:::stop_input_type(...) 4. └─rlang::abort(message, ..., call = call, arg = arg) ── Error ('test_getClinDT.R:176:7'): Percentages are correctly formatted ─────── Error in `expect(cDefs[[which(cDefsFmtPercentage)]]$targets, 1)`: `ok` must be `TRUE` or `FALSE`, not the number 1. Backtrace: ▆ 1. └─testthat::expect(ok = cDefs[[which(cDefsFmtPercentage)]]$targets) at test_getClinDT.R:176:7 2. └─testthat:::check_bool(ok) 3. └─testthat:::stop_input_type(...) 4. └─rlang::abort(message, ..., call = call, arg = arg) [ FAIL 2 | WARN 1 | SKIP 2 | PASS 305 ] Error: ! Test failures. Execution halted Package: cnd Check: tests New result: ERROR Running ‘spelling.R’ [0s/0s] Running ‘testthat.R’ [3s/4s] Running the tests in ‘tests/testthat.R’ failed. Complete output: > # This file is part of the standard setup for testthat. > # It is recommended that you do not modify it. > # > # Where should you do additional test configuration? > # Learn more about the roles of various files in: > # * https://r-pkgs.org/testing-design.html#sec-tests-files-overview > # * https://testthat.r-lib.org/articles/special-files.html > > library(testthat) > library(cnd) > > test_check("cnd") Saving _problems/test-condition-231.R [ FAIL 1 | WARN 0 | SKIP 11 | PASS 81 ] ══ Skipped tests (11) ══════════════════════════════════════════════════════════ • On CRAN (11): 'test-condition.R:109:1', 'test-condition.R:179:1', 'test-document.R:33:3', 'test-document.R:37:1', 'test-handlers.R:1:1', 'test-handlers.R:11:1', 'test-print.R:1:1', 'test-print.R:24:1', 'test-register.R:40:1', 'test-register.R:44:1', 'test-utils.R:28:1' ══ Failed tests ════════════════════════════════════════════════════════════════ ── Failure ('test-condition.R:226:3'): cnd(condition) handling ───────────────── Expected exactly one failure and no successes. Actually succeeded 1 times [ FAIL 1 | WARN 0 | SKIP 11 | PASS 81 ] Error: ! Test failures. Execution halted Package: coenocliner Check: tests New result: ERROR Running ‘test-all.R’ [2s/2s] Running the tests in ‘tests/test-all.R’ failed. Complete output: > ## Test `coenocliner` using the `testthat` package > > ## Setup > library("testthat") > > ## Runs the tests in inst/tests > test_check("coenocliner") Loading required package: coenocliner This is coenocliner 0.2-3 Saving _problems/test-coenocline-25.R Saving _problems/test-coenocline-55.R [ FAIL 2 | WARN 0 | SKIP 0 | PASS 85 ] ══ Failed tests ════════════════════════════════════════════════════════════════ ── Error ('test-coenocline.R:25:5'): coenocline() returns an integer matrix ──── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(typeof(sim) == "integer", is_true()) at test-coenocline.R:25:5 ── Error ('test-coenocline.R:55:5'): coenocline() returns an integer matrix with x and y gradients ── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(typeof(sim) == "integer", is_true()) at test-coenocline.R:55:5 [ FAIL 2 | WARN 0 | SKIP 0 | PASS 85 ] Error: ! Test failures. Execution halted Package: conflr Check: tests New result: ERROR Running ‘testthat.R’ [3s/3s] Running the tests in ‘tests/testthat.R’ failed. Complete output: > # Copyright (C) 2019 LINE Corporation > # > # conflr is free software; you can redistribute it and/or modify it under the > # terms of the GNU General Public License as published by the Free Software > # Foundation, version 3. > # > # conflr is distributed in the hope that it will be useful, but WITHOUT ANY > # WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR > # A PARTICULAR PURPOSE. See for more details. > > library(testthat) > library(conflr) > > test_check("conflr") Saving _problems/test-embed-images-58.R Saving _problems/test-front-matter-39.R Saving _problems/test-front-matter-94.R Saving _problems/test-front-matter-138.R Saving _problems/test-front-matter-156.R Saving _problems/test-front-matter-172.R Saving _problems/test-front-matter-200.R Saving _problems/test-invalid-credential-36.R Saving _problems/test-render-41.R Saving _problems/test-render-71.R Saving _problems/test-toc-41.R Saving _problems/test-toc-87.R Saving _problems/test-utils-82.R Saving _problems/test-utils-98.R [ FAIL 14 | WARN 0 | SKIP 4 | PASS 92 ] ══ Skipped tests (4) ═══════════════════════════════════════════════════════════ • On CRAN (4): 'test-content.R:12:3', 'test-content.R:42:3', 'test-contentbody.R:12:3', 'test-utils.R:25:3' ══ Failed tests ════════════════════════════════════════════════════════════════ ── Error ('test-embed-images.R:58:3'): embed_images() works for non-ASCII dir ── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─conflr:::do_confl_create_post_from_Rmd(...) at test-embed-images.R:58:3 2. └─testthat::with_mock(...) at ./helpers.R:10:3 3. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 4. └─lifecycle:::deprecate_stop0(msg) 5. └─rlang::cnd_signal(...) ── Error ('test-front-matter.R:39:3'): confluence_settings can be set from front-matter ── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─conflr:::do_confl_create_post_from_Rmd(confl_upload_mock, Rmd_with_all_defaults) at test-front-matter.R:39:3 2. └─testthat::with_mock(...) at ./helpers.R:10:3 3. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 4. └─lifecycle:::deprecate_stop0(msg) 5. └─rlang::cnd_signal(...) ── Error ('test-front-matter.R:94:3'): confluence_settings$title is prior to title ── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─conflr:::do_confl_create_post_from_Rmd(confl_upload_mock, Rmd_with_two_titles) at test-front-matter.R:94:3 2. └─testthat::with_mock(...) at ./helpers.R:10:3 3. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 4. └─lifecycle:::deprecate_stop0(msg) 5. └─rlang::cnd_signal(...) ── Error ('test-front-matter.R:138:3'): confluence_settings can be specified partially ── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─conflr:::do_confl_create_post_from_Rmd(confl_upload_mock, Rmd_with_some_settings) at test-front-matter.R:138:3 2. └─testthat::with_mock(...) at ./helpers.R:10:3 3. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 4. └─lifecycle:::deprecate_stop0(msg) 5. └─rlang::cnd_signal(...) ── Error ('test-front-matter.R:153:3'): supported_syntax_highlighting can be set via option ── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. ├─withr::with_options(...) at test-front-matter.R:153:3 2. │ └─base::force(code) 3. └─conflr:::do_confl_create_post_from_Rmd(confl_upload_mock, Rmd_with_some_settings) 4. └─testthat::with_mock(...) at ./helpers.R:10:3 5. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 6. └─lifecycle:::deprecate_stop0(msg) 7. └─rlang::cnd_signal(...) ── Error ('test-front-matter.R:172:3'): confluence_settings raise an error when any of mandatory parameters are missing ── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─conflr:::do_confl_create_post_from_Rmd(...) at test-front-matter.R:172:3 2. └─testthat::with_mock(...) at ./helpers.R:10:3 3. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 4. └─lifecycle:::deprecate_stop0(msg) 5. └─rlang::cnd_signal(...) ── Error ('test-front-matter.R:198:3'): confluence_settings are accepted for backward-compatibility ── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. ├─testthat::expect_warning(...) at test-front-matter.R:198:3 2. │ └─testthat:::quasi_capture(...) 3. │ ├─testthat (local) .capture(...) 4. │ │ └─base::withCallingHandlers(...) 5. │ └─rlang::eval_bare(quo_get_expr(.quo), quo_get_env(.quo)) 6. └─conflr:::do_confl_create_post_from_Rmd(confl_upload_mock, Rmd_deprecated) 7. └─testthat::with_mock(...) at ./helpers.R:10:3 8. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 9. └─lifecycle:::deprecate_stop0(msg) 10. └─rlang::cnd_signal(...) ── Failure ('test-invalid-credential.R:26:3'): confluence_document() stops early ── `with_mock(...)` threw an error with unexpected message. Expected match: "Invalid credentials!" Actual message: "`with_mock()` was deprecated in testthat 3.2.0 and is now defunct.\nℹ Please use `with_mocked_bindings()` instead." Backtrace: ▆ 1. ├─testthat::expect_error(...) at test-invalid-credential.R:26:3 2. │ └─testthat:::quasi_capture(...) 3. │ ├─testthat (local) .capture(...) 4. │ │ └─base::withCallingHandlers(...) 5. │ └─rlang::eval_bare(quo_get_expr(.quo), quo_get_env(.quo)) 6. └─testthat::with_mock(...) 7. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 8. └─lifecycle:::deprecate_stop0(msg) 9. └─rlang::cnd_signal(...) ── Error ('test-render.R:31:3'): rmarkdown::render() works when no space_key ─── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test-render.R:31:3 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) ── Error ('test-render.R:64:3'): rmarkdown::render() aborts when no space_key ── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test-render.R:64:3 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) ── Error ('test-toc.R:32:3'): TOC is added when set via argument ─────────────── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test-toc.R:32:3 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) ── Error ('test-toc.R:77:3'): TOC is added when set via front-matter ─────────── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test-toc.R:77:3 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) ── Error ('test-utils.R:77:3'): try_get_existing_page_id() works ─────────────── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test-utils.R:77:3 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) ── Error ('test-utils.R:93:3'): try_get_personal_space_key() handles personal spaces ── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test-utils.R:93:3 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) [ FAIL 14 | WARN 0 | SKIP 4 | PASS 92 ] Error: ! Test failures. Execution halted Package: countdown Check: tests New result: ERROR Running ‘testthat.R’ [3s/3s] Running the tests in ‘tests/testthat.R’ failed. Complete output: > library(testthat) > library(countdown) > > test_check("countdown") Saving _problems/test-shiny-10.R [ FAIL 1 | WARN 0 | SKIP 1 | PASS 43 ] ══ Skipped tests (1) ═══════════════════════════════════════════════════════════ • On CRAN (1): 'test-countdown.R:15:1' ══ Failed tests ════════════════════════════════════════════════════════════════ ── Error ('test-shiny.R:7:5'): countdown_app() / errors if shiny not available ── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. i Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(requireNamespace = function(...) FALSE, expect_error(countdown_app())) at test-shiny.R:7:5 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) [ FAIL 1 | WARN 0 | SKIP 1 | PASS 43 ] Error: ! Test failures. Execution halted Package: covdepGE Check: tests New result: ERROR Running ‘testthat.R’ [82s/55s] Running the tests in ‘tests/testthat.R’ failed. Complete output: > # This file is part of the standard setup for testthat. > # It is recommended that you do not modify it. > # > # Where should you do additional test configuration? > # Learn more about the roles of various files in: > # * https://r-pkgs.org/tests.html > # * https://testthat.r-lib.org/reference/test_package.html#special-files > > library(testthat) > library(covdepGE) > > test_check("covdepGE") Saving _problems/test-covdepGE-347.R Saving _problems/test-covdepGE-352.R Saving _problems/test-covdepGE-357.R Saving _problems/test-covdepGE-362.R Saving _problems/test-covdepGE-367.R Saving _problems/test-covdepGE-372.R Saving _problems/test-covdepGE-377.R Saving _problems/test-covdepGE-382.R Saving _problems/test-covdepGE-387.R Saving _problems/test-covdepGE-392.R Saving _problems/test-covdepGE-421.R Saving _problems/test-covdepGE-426.R Saving _problems/test-covdepGE-428.R Saving _problems/test-covdepGE-433.R Saving _problems/test-covdepGE-435.R Saving _problems/test-covdepGE-440.R Saving _problems/test-covdepGE-442.R Saving _problems/test-covdepGE-451.R Saving _problems/test-covdepGE-457.R Saving _problems/test-covdepGE-463.R Saving _problems/test-covdepGE-469.R Saving _problems/test-covdepGE-475.R Saving _problems/test-covdepGE-481.R Saving _problems/test-covdepGE-507.R Saving _problems/test-covdepGE-512.R [ FAIL 25 | WARN 13 | SKIP 31 | PASS 57 ] ══ Skipped tests (31) ══════════════════════════════════════════════════════════ • On CRAN (31): 'test-covdepGE.R:7:3', 'test-covdepGE.R:174:3', 'test-covdepGE.R:181:3', 'test-covdepGE.R:336:1', 'test-covdepGE.R:341:1', 'test-covdepGE.R:345:1', 'test-covdepGE.R:350:1', 'test-covdepGE.R:355:1', 'test-covdepGE.R:360:1', 'test-covdepGE.R:365:1', 'test-covdepGE.R:370:1', 'test-covdepGE.R:375:1', 'test-covdepGE.R:380:1', 'test-covdepGE.R:385:1', 'test-covdepGE.R:390:1', 'test-covdepGE.R:408:1', 'test-covdepGE.R:414:1', 'test-covdepGE.R:419:1', 'test-covdepGE.R:424:1', 'test-covdepGE.R:431:1', 'test-covdepGE.R:438:1', 'test-covdepGE.R:445:1', 'test-covdepGE.R:454:1', 'test-covdepGE.R:460:1', 'test-covdepGE.R:466:1', 'test-covdepGE.R:472:1', 'test-covdepGE.R:478:1', 'test-covdepGE.R:489:1', 'test-covdepGE.R:497:1', 'test-covdepGE.R:505:1', 'test-covdepGE.R:510:1' ══ Failed tests ════════════════════════════════════════════════════════════════ ── Failure ('test-covdepGE.R:346:3'): Different vertices give different plots ── Expected exactly one failure and no successes. Actually failed 0 times ── Failure ('test-covdepGE.R:351:3'): Different vertices give different plots ── Expected exactly one failure and no successes. Actually failed 0 times ── Failure ('test-covdepGE.R:356:3'): Different line types ───────────────────── Expected exactly one failure and no successes. Actually failed 0 times ── Failure ('test-covdepGE.R:361:3'): Different line size ────────────────────── Expected exactly one failure and no successes. Actually failed 0 times ── Failure ('test-covdepGE.R:366:3'): Different line color ───────────────────── Expected exactly one failure and no successes. Actually failed 0 times ── Failure ('test-covdepGE.R:371:3'): Different line color ───────────────────── Expected exactly one failure and no successes. Actually failed 0 times ── Failure ('test-covdepGE.R:376:3'): Different point shape ──────────────────── Expected exactly one failure and no successes. Actually failed 0 times ── Failure ('test-covdepGE.R:381:3'): Different point size ───────────────────── Expected exactly one failure and no successes. Actually failed 0 times ── Failure ('test-covdepGE.R:386:3'): Different point color ──────────────────── Expected exactly one failure and no successes. Actually failed 0 times ── Failure ('test-covdepGE.R:391:3'): Different point fill ───────────────────── Expected exactly one failure and no successes. Actually failed 0 times ── Failure ('test-covdepGE.R:420:3'): Row and column names are added back ────── Expected exactly one failure and no successes. Actually failed 0 times ── Failure ('test-covdepGE.R:425:3'): Different color 1 ──────────────────────── Expected exactly one failure and no successes. Actually failed 0 times ── Failure ('test-covdepGE.R:427:3'): Different color 1 ──────────────────────── Expected exactly one failure and no successes. Actually failed 0 times ── Failure ('test-covdepGE.R:432:3'): Different color 2 ──────────────────────── Expected exactly one failure and no successes. Actually failed 0 times ── Failure ('test-covdepGE.R:434:3'): Different color 2 ──────────────────────── Expected exactly one failure and no successes. Actually failed 0 times ── Failure ('test-covdepGE.R:439:3'): Different grid color ───────────────────── Expected exactly one failure and no successes. Actually failed 0 times ── Failure ('test-covdepGE.R:441:3'): Different grid color ───────────────────── Expected exactly one failure and no successes. Actually failed 0 times ── Failure ('test-covdepGE.R:450:3'): Include cell values ────────────────────── Expected exactly one failure and no successes. Actually failed 0 times ── Failure ('test-covdepGE.R:455:3'): Precision ──────────────────────────────── Expected exactly one failure and no successes. Actually failed 0 times ── Failure ('test-covdepGE.R:461:3'): Font size ──────────────────────────────── Expected exactly one failure and no successes. Actually failed 0 times ── Failure ('test-covdepGE.R:467:3'): Font color 1 ───────────────────────────── Expected exactly one failure and no successes. Actually failed 0 times ── Failure ('test-covdepGE.R:473:3'): Font color 2 ───────────────────────────── Expected exactly one failure and no successes. Actually failed 0 times ── Failure ('test-covdepGE.R:479:3'): Font threshold ─────────────────────────── Expected exactly one failure and no successes. Actually failed 0 times ── Failure ('test-covdepGE.R:506:3'): Different graph_colors ─────────────────── Expected exactly one failure and no successes. Actually failed 0 times ── Failure ('test-covdepGE.R:511:3'): No title summary ───────────────────────── Expected exactly one failure and no successes. Actually failed 0 times [ FAIL 25 | WARN 13 | SKIP 31 | PASS 57 ] Error: ! Test failures. Execution halted Package: deepregression Check: tests New result: ERROR Running ‘testthat.R’ [365s/365s] Running the tests in ‘tests/testthat.R’ failed. Complete output: > library(testthat) > library(deepregression) Loading required package: tensorflow Loading required package: tfprobability Loading required package: keras The keras package is deprecated. Use the keras3 package instead. > > if (reticulate::py_module_available("tensorflow") & + reticulate::py_module_available("keras") & + .Platform$OS.type != "windows"){ + test_check("deepregression") + } Downloading tensorflow-probability (6.7MiB) Downloading numpy (17.1MiB) Downloading tensorboard (5.2MiB) Downloading tensorflow (615.1MiB) Downloading tf-keras (1.6MiB) Downloading tf-keras Downloading tensorboard Downloading numpy Downloading tensorflow-probability Downloading tensorflow Installed 43 packages in 519ms 2025-11-12 05:08:54.976015: I external/local_xla/xla/tsl/cuda/cudart_stub.cc:32] Could not find cuda drivers on your machine, GPU will not be used. 2025-11-12 05:08:54.980974: I external/local_xla/xla/tsl/cuda/cudart_stub.cc:32] Could not find cuda drivers on your machine, GPU will not be used. 2025-11-12 05:08:54.999329: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered WARNING: All log messages before absl::InitializeLog() is called are written to STDERR E0000 00:00:1762920535.029731 941654 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered E0000 00:00:1762920535.038839 941654 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered W0000 00:00:1762920535.060938 941654 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once. W0000 00:00:1762920535.060987 941654 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once. W0000 00:00:1762920535.060991 941654 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once. W0000 00:00:1762920535.060994 941654 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once. 2025-11-12 05:08:55.067481: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations. To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags. Saving _problems/test_customtraining_torch-6.R Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz 8192/11490434 [..............................] - ETA: 0s 16384/11490434 [..............................] - ETA: 57s 49152/11490434 [..............................] - ETA: 45s 81920/11490434 [..............................] - ETA: 42s 131072/11490434 [..............................] - ETA: 31s 180224/11490434 [..............................] - ETA: 26s 245760/11490434 [..............................] - ETA: 21s 360448/11490434 [..............................] - ETA: 16s 475136/11490434 [>.............................] - ETA: 13s 688128/11490434 [>.............................] - ETA: 9s  950272/11490434 [=>............................] - ETA: 7s 1392640/11490434 [==>...........................] - ETA: 5s 1998848/11490434 [====>.........................] - ETA: 3s 2850816/11490434 [======>.......................] - ETA: 2s 4038656/11490434 [=========>....................] - ETA: 1s 5799936/11490434 [==============>...............] - ETA: 0s 8044544/11490434 [====================>.........] - ETA: 0s 9830400/11490434 [========================>.....] - ETA: 0s 11490434/11490434 [==============================] - 1s 0us/step Saving _problems/test_data_handler_torch-78.R 2025-11-12 05:09:21.085026: E external/local_xla/xla/stream_executor/cuda/cuda_platform.cc:51] failed call to cuInit: INTERNAL: CUDA error: Failed call to cuInit: UNKNOWN ERROR (303) Epoch 1/2 1/15 [=>............................] - ETA: 26s - loss: 2.2606 8/15 [===============>..............] - ETA: 0s - loss: 2.2283  15/15 [==============================] - 2s 41ms/step - loss: 2.1956 - val_loss: 2.1102 Epoch 2/2 1/15 [=>............................] - ETA: 0s - loss: 2.1033 15/15 [==============================] - ETA: 0s - loss: 2.0513 15/15 [==============================] - 0s 9ms/step - loss: 2.0513 - val_loss: 1.9745 Epoch 1/2 1/15 [=>............................] - ETA: 13s - loss: 2.6629WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0019s vs `on_train_batch_end` time: 0.0022s). Check your callbacks.  15/15 [==============================] - 1s 26ms/step - loss: 2.6533 - val_loss: 2.6393 Epoch 2/2 1/15 [=>............................] - ETA: 0s - loss: 2.6358 15/15 [==============================] - 0s 6ms/step - loss: 2.6365 - val_loss: 2.6227 Epoch 1/2 1/15 [=>............................] - ETA: 12s - loss: 2.9965 15/15 [==============================] - 1s 23ms/step - loss: 2.8998 - val_loss: 1.6201 Epoch 2/2 1/15 [=>............................] - ETA: 0s - loss: 2.1770 15/15 [==============================] - 0s 4ms/step - loss: 2.6616 - val_loss: 1.5446 Epoch 1/2 1/15 [=>............................] - ETA: 17s - loss: 4.1701WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0009s vs `on_train_batch_end` time: 0.0011s). Check your callbacks.  15/15 [==============================] - 1s 16ms/step - loss: 4.0062 - val_loss: 3.4240 Epoch 2/2 1/15 [=>............................] - ETA: 0s - loss: 4.3026 15/15 [==============================] - 0s 5ms/step - loss: 3.9103 - val_loss: 3.3606 Epoch 1/3 1/3 [=========>....................] - ETA: 1s - loss: 7.9864 2/3 [===================>..........] - ETA: 0s - loss: 8.9678 3/3 [==============================] - 1s 153ms/step - loss: 8.7055 - val_loss: 7.2396 Epoch 2/3 1/3 [=========>....................] - ETA: 0s - loss: 9.5053 3/3 [==============================] - 0s 23ms/step - loss: 8.6326 - val_loss: 7.1833 Epoch 3/3 1/3 [=========>....................] - ETA: 0s - loss: 8.5150WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0018s vs `on_train_batch_end` time: 0.0146s). Check your callbacks.  3/3 [==============================] - 0s 24ms/step - loss: 8.5566 - val_loss: 7.1282 Epoch 1/2 1/15 [=>............................] - ETA: 17s - loss: 2.6300 15/15 [==============================] - 2s 22ms/step - loss: 2.6282 - val_loss: 2.6186 Epoch 2/2 1/15 [=>............................] - ETA: 0s - loss: 2.6212 15/15 [==============================] - 0s 5ms/step - loss: 2.6124 - val_loss: 2.6029 Epoch 1/2 1/15 [=>............................] - ETA: 15s - loss: 653.6500 7/15 [=============>................] - ETA: 0s - loss: 1286.6836 15/15 [==============================] - 2s 32ms/step - loss: 1248.0042 - val_loss: 1370.1056 Epoch 2/2 1/15 [=>............................] - ETA: 0s - loss: 755.2899 15/15 [==============================] - ETA: 0s - loss: 960.7450 15/15 [==============================] - 0s 7ms/step - loss: 960.7450 - val_loss: 1055.7156 Epoch 1/2 1/15 [=>............................] - ETA: 15s - loss: 927.8140 11/15 [=====================>........] - ETA: 0s - loss: 2320.8728 15/15 [==============================] - 2s 29ms/step - loss: 3026.7996 - val_loss: 321.5523 Epoch 2/2 1/15 [=>............................] - ETA: 0s - loss: 991.2161 15/15 [==============================] - ETA: 0s - loss: 2679.4141 15/15 [==============================] - 0s 7ms/step - loss: 2679.4141 - val_loss: 292.7625 Epoch 1/2 1/15 [=>............................] - ETA: 17s - loss: 2.3149 12/15 [=======================>......] - ETA: 0s - loss: 2.2581  15/15 [==============================] - 2s 29ms/step - loss: 2.2516 - val_loss: 2.2750 Epoch 2/2 1/15 [=>............................] - ETA: 0s - loss: 2.1948 15/15 [==============================] - ETA: 0s - loss: 2.2132 15/15 [==============================] - 0s 7ms/step - loss: 2.2132 - val_loss: 2.2328 Saving _problems/test_deepregression_torch-10.R Saving _problems/test_deepregression_torch-117.R Saving _problems/test_deepregression_torch-158.R Saving _problems/test_deepregression_torch-190.R Saving _problems/test_deepregression_torch-229.R Fitting member 1 ...Epoch 1/10 1/32 [..............................] - ETA: 28s - loss: 2.3293 19/32 [================>.............] - ETA: 0s - loss: 2.3442  32/32 [==============================] - 1s 3ms/step - loss: 2.3303 Epoch 2/10 1/32 [..............................] - ETA: 0s - loss: 2.3269 18/32 [===============>..............] - ETA: 0s - loss: 2.3110 32/32 [==============================] - 0s 3ms/step - loss: 2.2977 Epoch 3/10 1/32 [..............................] - ETA: 0s - loss: 2.3270 20/32 [=================>............] - ETA: 0s - loss: 2.2776 32/32 [==============================] - 0s 3ms/step - loss: 2.2650 Epoch 4/10 1/32 [..............................] - ETA: 0s - loss: 2.2270 17/32 [==============>...............] - ETA: 0s - loss: 2.2258 32/32 [==============================] - 0s 3ms/step - loss: 2.2325 Epoch 5/10 1/32 [..............................] - ETA: 0s - loss: 2.2185 27/32 [========================>.....] - ETA: 0s - loss: 2.2045 32/32 [==============================] - 0s 2ms/step - loss: 2.2002 Epoch 6/10 1/32 [..............................] - ETA: 0s - loss: 2.1431 23/32 [====================>.........] - ETA: 0s - loss: 2.1730 32/32 [==============================] - 0s 2ms/step - loss: 2.1681 Epoch 7/10 1/32 [..............................] - ETA: 0s - loss: 2.2840 24/32 [=====================>........] - ETA: 0s - loss: 2.1375 32/32 [==============================] - 0s 2ms/step - loss: 2.1359 Epoch 8/10 1/32 [..............................] - ETA: 0s - loss: 2.0769 19/32 [================>.............] - ETA: 0s - loss: 2.1104 32/32 [==============================] - 0s 3ms/step - loss: 2.1036 Epoch 9/10 1/32 [..............................] - ETA: 0s - loss: 2.0602 22/32 [===================>..........] - ETA: 0s - loss: 2.0763 32/32 [==============================] - 0s 3ms/step - loss: 2.0717 Epoch 10/10 1/32 [..............................] - ETA: 0s - loss: 2.0980 23/32 [====================>.........] - ETA: 0s - loss: 2.0373 32/32 [==============================] - 0s 2ms/step - loss: 2.0400 Done in 1.968095 secs Fitting member 2 ...Epoch 1/10 1/32 [..............................] - ETA: 0s - loss: 2.1814 21/32 [==================>...........] - ETA: 0s - loss: 2.3467 32/32 [==============================] - 0s 3ms/step - loss: 2.3312 Epoch 2/10 1/32 [..............................] - ETA: 0s - loss: 2.4545 20/32 [=================>............] - ETA: 0s - loss: 2.3068 32/32 [==============================] - 0s 3ms/step - loss: 2.2785 Epoch 3/10 1/32 [..............................] - ETA: 0s - loss: 2.5406 19/32 [================>.............] - ETA: 0s - loss: 2.2776 32/32 [==============================] - 0s 3ms/step - loss: 2.2334 Epoch 4/10 1/32 [..............................] - ETA: 0s - loss: 2.0854 17/32 [==============>...............] - ETA: 0s - loss: 2.1692 32/32 [==============================] - 0s 3ms/step - loss: 2.1937 Epoch 5/10 1/32 [..............................] - ETA: 0s - loss: 2.2393 19/32 [================>.............] - ETA: 0s - loss: 2.1951 32/32 [==============================] - 0s 3ms/step - loss: 2.1597 Epoch 6/10 1/32 [..............................] - ETA: 0s - loss: 2.0225 25/32 [======================>.......] - ETA: 0s - loss: 2.1440 32/32 [==============================] - 0s 2ms/step - loss: 2.1276 Epoch 7/10 1/32 [..............................] - ETA: 0s - loss: 2.4568 32/32 [==============================] - 0s 2ms/step - loss: 2.0961 Epoch 8/10 1/32 [..............................] - ETA: 0s - loss: 1.9680 32/32 [==============================] - 0s 2ms/step - loss: 2.0644 Epoch 9/10 1/32 [..............................] - ETA: 0s - loss: 2.0730 32/32 [==============================] - 0s 1ms/step - loss: 2.0336 Epoch 10/10 1/32 [..............................] - ETA: 0s - loss: 2.1117 32/32 [==============================] - 0s 1ms/step - loss: 2.0026 Done in 0.9229863 secs Fitting member 3 ...Epoch 1/10 1/32 [..............................] - ETA: 0s - loss: 41.9180 24/32 [=====================>........] - ETA: 0s - loss: 40.3070 32/32 [==============================] - 0s 2ms/step - loss: 39.2828 Epoch 2/10 1/32 [..............................] - ETA: 0s - loss: 34.1315 23/32 [====================>.........] - ETA: 0s - loss: 27.6452 32/32 [==============================] - 0s 2ms/step - loss: 27.2884 Epoch 3/10 1/32 [..............................] - ETA: 0s - loss: 27.6960 32/32 [==============================] - ETA: 0s - loss: 21.7021 32/32 [==============================] - 0s 2ms/step - loss: 21.7021 Epoch 4/10 1/32 [..............................] - ETA: 0s - loss: 13.7574 24/32 [=====================>........] - ETA: 0s - loss: 18.5897 32/32 [==============================] - 0s 2ms/step - loss: 18.2036 Epoch 5/10 1/32 [..............................] - ETA: 0s - loss: 15.2942 21/32 [==================>...........] - ETA: 0s - loss: 16.1793 32/32 [==============================] - 0s 2ms/step - loss: 15.8282 Epoch 6/10 1/32 [..............................] - ETA: 0s - loss: 9.8128 29/32 [==========================>...] - ETA: 0s - loss: 14.0164 32/32 [==============================] - 0s 2ms/step - loss: 14.0666 Epoch 7/10 1/32 [..............................] - ETA: 0s - loss: 14.0067 24/32 [=====================>........] - ETA: 0s - loss: 12.9735 32/32 [==============================] - 0s 2ms/step - loss: 12.6950 Epoch 8/10 1/32 [..............................] - ETA: 0s - loss: 12.2746 25/32 [======================>.......] - ETA: 0s - loss: 11.4488 32/32 [==============================] - 0s 3ms/step - loss: 11.5684 Epoch 9/10 1/32 [..............................] - ETA: 0s - loss: 13.3544 21/32 [==================>...........] - ETA: 0s - loss: 10.6431 32/32 [==============================] - 0s 3ms/step - loss: 10.6440 Epoch 10/10 1/32 [..............................] - ETA: 0s - loss: 8.5922 22/32 [===================>..........] - ETA: 0s - loss: 9.4548 32/32 [==============================] - 0s 3ms/step - loss: 9.8495 Done in 0.8690994 secs Fitting member 4 ...Epoch 1/10 1/32 [..............................] - ETA: 0s - loss: 2.8816 22/32 [===================>..........] - ETA: 0s - loss: 2.9627 32/32 [==============================] - 0s 2ms/step - loss: 2.9588 Epoch 2/10 1/32 [..............................] - ETA: 0s - loss: 2.3825 25/32 [======================>.......] - ETA: 0s - loss: 2.9174 32/32 [==============================] - 0s 2ms/step - loss: 2.9011 Epoch 3/10 1/32 [..............................] - ETA: 0s - loss: 3.9315 26/32 [=======================>......] - ETA: 0s - loss: 2.9154 32/32 [==============================] - 0s 2ms/step - loss: 2.8534 Epoch 4/10 1/32 [..............................] - ETA: 0s - loss: 2.3602 24/32 [=====================>........] - ETA: 0s - loss: 2.7642 32/32 [==============================] - 0s 3ms/step - loss: 2.8062 Epoch 5/10 1/32 [..............................] - ETA: 0s - loss: 2.7419 15/32 [=============>................] - ETA: 0s - loss: 2.8385 32/32 [==============================] - 0s 4ms/step - loss: 2.7623 Epoch 6/10 1/32 [..............................] - ETA: 0s - loss: 1.7251 32/32 [==============================] - 0s 2ms/step - loss: 2.7193 Epoch 7/10 1/32 [..............................] - ETA: 0s - loss: 2.8260 32/32 [==============================] - 0s 2ms/step - loss: 2.6774 Epoch 8/10 1/32 [..............................] - ETA: 0s - loss: 2.2710 32/32 [==============================] - 0s 1ms/step - loss: 2.6383 Epoch 9/10 1/32 [..............................] - ETA: 0s - loss: 3.0022 25/32 [======================>.......] - ETA: 0s - loss: 2.7221 32/32 [==============================] - 0s 2ms/step - loss: 2.6007 Epoch 10/10 1/32 [..............................] - ETA: 0s - loss: 2.6980 21/32 [==================>...........] - ETA: 0s - loss: 2.4006 32/32 [==============================] - 0s 3ms/step - loss: 2.5630 Done in 0.8861516 secs Fitting member 5 ...Epoch 1/10 1/32 [..............................] - ETA: 0s - loss: 112.3703 25/32 [======================>.......] - ETA: 0s - loss: 143.2147 32/32 [==============================] - 0s 2ms/step - loss: 139.0890 Epoch 2/10 1/32 [..............................] - ETA: 0s - loss: 141.4559 27/32 [========================>.....] - ETA: 0s - loss: 98.0714  32/32 [==============================] - 0s 2ms/step - loss: 95.6168 Epoch 3/10 1/32 [..............................] - ETA: 0s - loss: 97.2787 21/32 [==================>...........] - ETA: 0s - loss: 80.1010 32/32 [==============================] - 0s 3ms/step - loss: 74.6476 Epoch 4/10 1/32 [..............................] - ETA: 0s - loss: 42.6719 20/32 [=================>............] - ETA: 0s - loss: 64.3395 32/32 [==============================] - 0s 3ms/step - loss: 61.8040 Epoch 5/10 1/32 [..............................] - ETA: 0s - loss: 51.5790 21/32 [==================>...........] - ETA: 0s - loss: 53.9275 32/32 [==============================] - 0s 3ms/step - loss: 53.1899 Epoch 6/10 1/32 [..............................] - ETA: 0s - loss: 35.5863 24/32 [=====================>........] - ETA: 0s - loss: 46.4906 32/32 [==============================] - 0s 2ms/step - loss: 46.9254 Epoch 7/10 1/32 [..............................] - ETA: 0s - loss: 43.7696 23/32 [====================>.........] - ETA: 0s - loss: 43.0117 32/32 [==============================] - 0s 2ms/step - loss: 42.0743 Epoch 8/10 1/32 [..............................] - ETA: 0s - loss: 43.0723 24/32 [=====================>........] - ETA: 0s - loss: 37.5887 32/32 [==============================] - 0s 3ms/step - loss: 38.1152 Epoch 9/10 1/32 [..............................] - ETA: 0s - loss: 45.3285 24/32 [=====================>........] - ETA: 0s - loss: 35.3619 32/32 [==============================] - 0s 2ms/step - loss: 34.8808 Epoch 10/10 1/32 [..............................] - ETA: 0s - loss: 29.3974 24/32 [=====================>........] - ETA: 0s - loss: 31.5511 32/32 [==============================] - 0s 2ms/step - loss: 32.1088 Done in 1.00689 secs Epoch 1/2 1/3 [=========>....................] - ETA: 2s - loss: 2.3341 3/3 [==============================] - 1s 157ms/step - loss: 2.3038 - val_loss: 2.2154 Epoch 2/2 1/3 [=========>....................] - ETA: 0s - loss: 2.3662 3/3 [==============================] - 0s 20ms/step - loss: 2.3004 - val_loss: 2.2128 Epoch 1/2 1/3 [=========>....................] - ETA: 0s - loss: 52.8941 3/3 [==============================] - 0s 53ms/step - loss: 47.0024 - val_loss: 27.1291 Epoch 2/2 1/3 [=========>....................] - ETA: 0s - loss: 49.6579 3/3 [==============================] - 0s 23ms/step - loss: 46.6172 - val_loss: 26.8568 Saving _problems/test_ensemble_torch-17.R Saving _problems/test_ensemble_torch-63.R Fitting normal Fitting bernoulli Fitting bernoulli_prob WARNING:tensorflow:5 out of the last 13 calls to .test_function at 0x7fda28e46700> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details. Fitting beta WARNING:tensorflow:5 out of the last 11 calls to .test_function at 0x7fda28dcfec0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details. Fitting betar Fitting chi2 Fitting chi Fitting exponential Fitting gamma Fitting gammar Fitting gumbel Fitting half_normal Fitting horseshoe Fitting inverse_gaussian Fitting laplace Fitting log_normal Fitting logistic Fitting negbinom Fitting negbinom Fitting pareto_ls Fitting poisson Fitting poisson_lograte Saving _problems/test_families_torch-82.R Saving _problems/test_layers_torch-6.R Saving _problems/test_methods_torch-23.R Epoch 1/2 1/29 [>.............................] - ETA: 27s - loss: 11.2004 19/29 [==================>...........] - ETA: 0s - loss: 11.0986  29/29 [==============================] - 1s 14ms/step - loss: 10.6607 - val_loss: 7.6350 Epoch 2/2 1/29 [>.............................] - ETA: 0s - loss: 7.1379 21/29 [====================>.........] - ETA: 0s - loss: 9.6194 29/29 [==============================] - 0s 4ms/step - loss: 9.5296 - val_loss: 6.8647 Epoch 1/10 1/29 [>.............................] - ETA: 1:11 - loss: 6.4103 12/29 [===========>..................] - ETA: 0s - loss: 8.5702  23/29 [======================>.......] - ETA: 0s - loss: 8.1863 29/29 [==============================] - 3s 28ms/step - loss: 7.8712 - val_loss: 8.8751 Epoch 2/10 1/29 [>.............................] - ETA: 0s - loss: 6.9897 11/29 [==========>...................] - ETA: 0s - loss: 7.5382 23/29 [======================>.......] - ETA: 0s - loss: 7.5970 29/29 [==============================] - 0s 6ms/step - loss: 7.4694 - val_loss: 8.4332 Epoch 3/10 1/29 [>.............................] - ETA: 0s - loss: 14.7798 11/29 [==========>...................] - ETA: 0s - loss: 8.7902  20/29 [===================>..........] - ETA: 0s - loss: 7.4106 29/29 [==============================] - 0s 7ms/step - loss: 7.1170 - val_loss: 8.0236 Epoch 4/10 1/29 [>.............................] - ETA: 0s - loss: 9.3139 14/29 [=============>................] - ETA: 0s - loss: 6.6848 27/29 [==========================>...] - ETA: 0s - loss: 6.8854 29/29 [==============================] - 0s 5ms/step - loss: 6.7865 - val_loss: 7.6687 Epoch 5/10 1/29 [>.............................] - ETA: 0s - loss: 3.8834 12/29 [===========>..................] - ETA: 0s - loss: 7.0123 23/29 [======================>.......] - ETA: 0s - loss: 6.6037 29/29 [==============================] - 0s 7ms/step - loss: 6.4899 - val_loss: 7.3231 Epoch 6/10 1/29 [>.............................] - ETA: 0s - loss: 5.2272 11/29 [==========>...................] - ETA: 0s - loss: 6.8838 23/29 [======================>.......] - ETA: 0s - loss: 6.2762 29/29 [==============================] - 0s 6ms/step - loss: 6.2000 - val_loss: 6.9819 Epoch 7/10 1/29 [>.............................] - ETA: 0s - loss: 6.7016 11/29 [==========>...................] - ETA: 0s - loss: 6.1623 22/29 [=====================>........] - ETA: 0s - loss: 6.0533 29/29 [==============================] - 0s 7ms/step - loss: 5.9123 - val_loss: 6.6514 Epoch 8/10 1/29 [>.............................] - ETA: 0s - loss: 5.7023 11/29 [==========>...................] - ETA: 0s - loss: 5.6565 18/29 [=================>............] - ETA: 0s - loss: 5.6322 26/29 [=========================>....] - ETA: 0s - loss: 5.6011 29/29 [==============================] - 0s 8ms/step - loss: 5.6402 - val_loss: 6.3442 Epoch 9/10 1/29 [>.............................] - ETA: 0s - loss: 4.7939 11/29 [==========>...................] - ETA: 0s - loss: 5.3717 20/29 [===================>..........] - ETA: 0s - loss: 5.5627 29/29 [==============================] - 0s 7ms/step - loss: 5.3794 - val_loss: 6.0578 Epoch 10/10 1/29 [>.............................] - ETA: 0s - loss: 4.1903 12/29 [===========>..................] - ETA: 0s - loss: 5.0002 24/29 [=======================>......] - ETA: 0s - loss: 5.2108 29/29 [==============================] - 0s 6ms/step - loss: 5.1438 - val_loss: 5.7953 Epoch 1/10 1/29 [>.............................] - ETA: 1:09 - loss: 1.4933 13/29 [============>.................] - ETA: 0s - loss: 1.4895  24/29 [=======================>......] - ETA: 0s - loss: 1.4861 29/29 [==============================] - 3s 27ms/step - loss: 1.4848 - val_loss: 1.4755 Epoch 2/10 1/29 [>.............................] - ETA: 0s - loss: 1.4755 11/29 [==========>...................] - ETA: 0s - loss: 1.4726 22/29 [=====================>........] - ETA: 0s - loss: 1.4696 29/29 [==============================] - 0s 6ms/step - loss: 1.4679 - val_loss: 1.4596 Epoch 3/10 1/29 [>.............................] - ETA: 0s - loss: 1.4597 15/29 [==============>...............] - ETA: 0s - loss: 1.4562 29/29 [==============================] - 0s 5ms/step - loss: 1.4531 - val_loss: 1.4458 Epoch 4/10 1/29 [>.............................] - ETA: 0s - loss: 1.4459 17/29 [================>.............] - ETA: 0s - loss: 1.4424 29/29 [==============================] - 0s 4ms/step - loss: 1.4401 - val_loss: 1.4336 Epoch 5/10 1/29 [>.............................] - ETA: 0s - loss: 1.4338 15/29 [==============>...............] - ETA: 0s - loss: 1.4311 26/29 [=========================>....] - ETA: 0s - loss: 1.4291 29/29 [==============================] - 0s 6ms/step - loss: 1.4287 - val_loss: 1.4230 Epoch 6/10 1/29 [>.............................] - ETA: 0s - loss: 1.4231 11/29 [==========>...................] - ETA: 0s - loss: 1.4215 22/29 [=====================>........] - ETA: 0s - loss: 1.4197 29/29 [==============================] - 0s 6ms/step - loss: 1.4187 - val_loss: 1.4137 Epoch 7/10 1/29 [>.............................] - ETA: 0s - loss: 1.4140 12/29 [===========>..................] - ETA: 0s - loss: 1.4123 21/29 [====================>.........] - ETA: 0s - loss: 1.4111 29/29 [==============================] - 0s 7ms/step - loss: 1.4101 - val_loss: 1.4059 Epoch 8/10 1/29 [>.............................] - ETA: 0s - loss: 1.4061 10/29 [=========>....................] - ETA: 0s - loss: 1.4050 20/29 [===================>..........] - ETA: 0s - loss: 1.4038 29/29 [==============================] - 0s 7ms/step - loss: 1.4030 - val_loss: 1.3995 Epoch 9/10 1/29 [>.............................] - ETA: 0s - loss: 1.3999 11/29 [==========>...................] - ETA: 0s - loss: 1.3988 21/29 [====================>.........] - ETA: 0s - loss: 1.3979 29/29 [==============================] - 0s 7ms/step - loss: 1.3973 - val_loss: 1.3948 Epoch 10/10 1/29 [>.............................] - ETA: 0s - loss: 1.3947 13/29 [============>.................] - ETA: 0s - loss: 1.3941 24/29 [=======================>......] - ETA: 0s - loss: 1.3935 29/29 [==============================] - 0s 6ms/step - loss: 1.3933 - val_loss: 1.3916 Epoch 1/10 1/29 [>.............................] - ETA: 1:22 - loss: 1.2453 9/29 [========>.....................] - ETA: 0s - loss: 1.1685  18/29 [=================>............] - ETA: 0s - loss: 1.1901 28/29 [===========================>..] - ETA: 0s - loss: 1.1832 29/29 [==============================] - 4s 27ms/step - loss: 1.1842 - val_loss: 2.1275 Epoch 2/10 1/29 [>.............................] - ETA: 0s - loss: 0.9681 11/29 [==========>...................] - ETA: 0s - loss: 1.1889 21/29 [====================>.........] - ETA: 0s - loss: 1.1765 29/29 [==============================] - 0s 7ms/step - loss: 1.1645 - val_loss: 2.0574 Epoch 3/10 1/29 [>.............................] - ETA: 0s - loss: 1.0562 11/29 [==========>...................] - ETA: 0s - loss: 1.1533 21/29 [====================>.........] - ETA: 0s - loss: 1.1260 29/29 [==============================] - 0s 7ms/step - loss: 1.1441 - val_loss: 1.9952 Epoch 4/10 1/29 [>.............................] - ETA: 0s - loss: 1.0159 9/29 [========>.....................] - ETA: 0s - loss: 1.1085 18/29 [=================>............] - ETA: 0s - loss: 1.1009 28/29 [===========================>..] - ETA: 0s - loss: 1.1213 29/29 [==============================] - 0s 7ms/step - loss: 1.1216 - val_loss: 1.9393 Epoch 5/10 1/29 [>.............................] - ETA: 0s - loss: 1.0572 12/29 [===========>..................] - ETA: 0s - loss: 1.1144 22/29 [=====================>........] - ETA: 0s - loss: 1.1126 29/29 [==============================] - 0s 7ms/step - loss: 1.0955 - val_loss: 1.8846 Epoch 6/10 1/29 [>.............................] - ETA: 0s - loss: 0.9186 11/29 [==========>...................] - ETA: 0s - loss: 1.0515 21/29 [====================>.........] - ETA: 0s - loss: 1.0574 29/29 [==============================] - 0s 7ms/step - loss: 1.0666 - val_loss: 1.8390 Epoch 7/10 1/29 [>.............................] - ETA: 0s - loss: 1.0423 11/29 [==========>...................] - ETA: 0s - loss: 1.0593 21/29 [====================>.........] - ETA: 0s - loss: 1.0509 29/29 [==============================] - 0s 7ms/step - loss: 1.0385 - val_loss: 1.7931 Epoch 8/10 1/29 [>.............................] - ETA: 0s - loss: 1.0082 11/29 [==========>...................] - ETA: 0s - loss: 1.0033 22/29 [=====================>........] - ETA: 0s - loss: 1.0098 29/29 [==============================] - 0s 7ms/step - loss: 1.0098 - val_loss: 1.7559 Epoch 9/10 1/29 [>.............................] - ETA: 0s - loss: 0.9752 10/29 [=========>....................] - ETA: 0s - loss: 1.0247 20/29 [===================>..........] - ETA: 0s - loss: 0.9961 29/29 [==============================] - 0s 7ms/step - loss: 0.9810 - val_loss: 1.7130 Epoch 10/10 1/29 [>.............................] - ETA: 0s - loss: 0.8496 10/29 [=========>....................] - ETA: 0s - loss: 0.9303 19/29 [==================>...........] - ETA: 0s - loss: 0.9456 29/29 [==============================] - 0s 7ms/step - loss: 0.9513 - val_loss: 1.6782 Model: "model_43" ________________________________________________________________________________ Layer (type) Output Shape Para Connected to Trainable m # ================================================================================ input_node_x1_x2_ [(None, 2)] 0 [] Y n_trees_2_n_layer s_3_tree_depth_5_ _1 (InputLayer) input__Intercept_ [(None, 1)] 0 [] Y _1 (InputLayer) node_2 (NODE) (None, 3) 1754 ['input_node_x1_x2 Y _n_trees_2_n_layer s_3_tree_depth_5__ 1[0][0]'] 1_1 (Dense) (None, 3) 3 ['input__Intercept Y __1[0][0]'] add_77 (Add) (None, 3) 0 ['node_2[0][0]', Y '1_1[0][0]'] distribution_lamb ((None, 3), 0 ['add_77[0][0]'] Y da_43 (Distributi (None, 3)) onLambda) ================================================================================ Total params: 1757 (6.86 KB) Trainable params: 793 (3.10 KB) Non-trainable params: 964 (3.77 KB) ________________________________________________________________________________ Model formulas: --------------- loc : ~node(x1, x2, n_trees = 2, n_layers = 3, tree_depth = 5) Fitting model with 1 orthogonalization(s) ... Fitting model with 2 orthogonalization(s) ... Fitting model with 3 orthogonalization(s) ... Fitting model with 4 orthogonalization(s) ... Fitting model with 5 orthogonalization(s) ... Saving _problems/test_reproducibility_torch-30.R Saving _problems/test_subnetwork_init_torch-20.R Fitting Fold 1 ... Done in 1.863178 secs Fitting Fold 2 ... Done in 0.3082445 secs Epoch 1/2 1/2 [==============>...............] - ETA: 0s - loss: 22.0463 2/2 [==============================] - 0s 13ms/step - loss: 22.4672 Epoch 2/2 1/2 [==============>...............] - ETA: 0s - loss: 21.8272 2/2 [==============================] - 0s 13ms/step - loss: 20.5671 Fitting Fold 1 ... Done in 1.983902 secs Fitting Fold 2 ... Done in 0.4002454 secs Epoch 1/2 1/2 [==============>...............] - ETA: 0s - loss: 22.0463 2/2 [==============================] - 0s 13ms/step - loss: 22.4672 Epoch 2/2 1/2 [==============>...............] - ETA: 0s - loss: 21.8272 2/2 [==============================] - 0s 13ms/step - loss: 20.5671 [ FAIL 14 | WARN 0 | SKIP 1 | PASS 680 ] ══ Skipped tests (1) ═══════════════════════════════════════════════════════════ • empty test (1): ══ Failed tests ════════════════════════════════════════════════════════════════ ── Error ('test_customtraining_torch.R:6:3'): Use multiple optimizers torch ──── Error in `(function (size, options, memory_format) { .Call(`_torch_cpp_torch_namespace_empty_size_IntArrayRef`, size, options, memory_format) })(size = list(50, 1), options = list(dtype = NULL, layout = NULL, device = NULL, requires_grad = FALSE), memory_format = NULL)`: Lantern is not loaded. Please use `install_torch()` to install additional dependencies. Backtrace: ▆ 1. └─torch::nn_linear(1, 50) at test_customtraining_torch.R:6:3 2. └─Module$new(...) 3. └─torch (local) initialize(...) 4. ├─torch::nn_parameter(torch_empty(out_features, in_features)) 5. │ └─torch:::is_torch_tensor(x) 6. └─torch::torch_empty(out_features, in_features) 7. ├─base::do.call(.torch_empty, args) 8. └─torch (local) ``(options = ``, size = ``) 9. └─torch:::call_c_function(...) 10. └─torch:::do_call(f, args) 11. ├─base::do.call(fun, args) 12. └─torch (local) ``(size = ``, options = ``, memory_format = NULL) ── Error ('test_data_handler_torch.R:75:3'): properties of dataset torch ─────── Error in `(function (size, options, memory_format) { .Call(`_torch_cpp_torch_namespace_empty_size_IntArrayRef`, size, options, memory_format) })(size = list(1L, 1L), options = list(dtype = NULL, layout = NULL, device = NULL, requires_grad = FALSE), memory_format = NULL)`: Lantern is not loaded. Please use `install_torch()` to install additional dependencies. Backtrace: ▆ 1. └─deepregression::deepregression(...) at test_data_handler_torch.R:75:3 2. └─base::lapply(...) 3. └─deepregression (local) FUN(X[[i]], ...) 4. └─subnetwork_builder[[i]](...) 5. └─base::lapply(...) 6. └─deepregression (local) FUN(X[[i]], ...) 7. └─pp_lay[[i]]$layer() 8. ├─base::do.call(layer_class, layer_args) 9. └─deepregression (local) ``(...) 10. └─torch (local) layer_module(...) 11. └─Module$new(...) 12. └─deepregression (local) initialize(...) 13. ├─torch::nn_parameter(torch_empty(out_features, in_features)) 14. │ └─torch:::is_torch_tensor(x) 15. └─torch::torch_empty(out_features, in_features) 16. ├─base::do.call(.torch_empty, args) 17. └─torch (local) ``(options = ``, size = ``) 18. └─torch:::call_c_function(...) 19. └─torch:::do_call(f, args) 20. ├─base::do.call(fun, args) 21. └─torch (local) ``(size = ``, options = ``, memory_format = NULL) ── Error ('test_deepregression_torch.R:6:5'): Simple additive model ──────────── Error in `(function (size, options, memory_format) { .Call(`_torch_cpp_torch_namespace_empty_size_IntArrayRef`, size, options, memory_format) })(size = list(2, 1), options = list(dtype = NULL, layout = NULL, device = NULL, requires_grad = FALSE), memory_format = NULL)`: Lantern is not loaded. Please use `install_torch()` to install additional dependencies. Backtrace: ▆ 1. └─deepregression::deepregression(...) at test_deepregression_torch.R:21:5 2. └─base::lapply(...) 3. └─deepregression (local) FUN(X[[i]], ...) 4. └─subnetwork_builder[[i]](...) 5. └─base::lapply(...) 6. └─deepregression (local) FUN(X[[i]], ...) 7. └─pp_lay[[i]]$layer() 8. ├─torch::nn_sequential(...) at test_deepregression_torch.R:6:5 9. │ └─Module$new(...) 10. │ └─torch (local) initialize(...) 11. │ └─rlang::list2(...) 12. └─torch::nn_linear(in_features = i, out_features = 2, bias = F) 13. └─Module$new(...) 14. └─torch (local) initialize(...) 15. ├─torch::nn_parameter(torch_empty(out_features, in_features)) 16. │ └─torch:::is_torch_tensor(x) 17. └─torch::torch_empty(out_features, in_features) 18. ├─base::do.call(.torch_empty, args) 19. └─torch (local) ``(options = ``, size = ``) 20. └─torch:::call_c_function(...) 21. └─torch:::do_call(f, args) 22. ├─base::do.call(fun, args) 23. └─torch (local) ``(size = ``, options = ``, memory_format = NULL) ── Error ('test_deepregression_torch.R:110:3'): Generalized additive model ───── Error in `torch_tensor_cpp(data, dtype, device, requires_grad, pin_memory)`: Lantern is not loaded. Please use `install_torch()` to install additional dependencies. Backtrace: ▆ 1. └─deepregression::deepregression(...) at test_deepregression_torch.R:110:3 2. └─base::lapply(...) 3. └─deepregression (local) FUN(X[[i]], ...) 4. └─subnetwork_builder[[i]](...) 5. └─base::lapply(...) 6. └─deepregression (local) FUN(X[[i]], ...) 7. └─pp_lay[[i]]$layer() 8. ├─base::do.call(layer_class, layer_args) 9. └─deepregression (local) ``(...) 10. └─torch::torch_tensor(P) 11. └─Tensor$new(data, dtype, device, requires_grad, pin_memory) 12. └─methods$initialize(NULL, NULL, ...) 13. └─torch:::torch_tensor_cpp(...) ── Error ('test_deepregression_torch.R:151:3'): Deep generalized additive model with LSS ── Error in `torch_tensor_cpp(data, dtype, device, requires_grad, pin_memory)`: Lantern is not loaded. Please use `install_torch()` to install additional dependencies. Backtrace: ▆ 1. └─deepregression::deepregression(...) at test_deepregression_torch.R:151:3 2. └─base::lapply(...) 3. └─deepregression (local) FUN(X[[i]], ...) 4. └─subnetwork_builder[[i]](...) 5. └─base::lapply(...) 6. └─deepregression (local) FUN(X[[i]], ...) 7. └─pp_lay[[i]]$layer() 8. ├─base::do.call(layer_class, layer_args) 9. └─deepregression (local) ``(...) 10. └─torch::torch_tensor(P) 11. └─Tensor$new(data, dtype, device, requires_grad, pin_memory) 12. └─methods$initialize(NULL, NULL, ...) 13. └─torch:::torch_tensor_cpp(...) ── Error ('test_deepregression_torch.R:181:3'): GAMs with shared weights ─────── Error in `torch_tensor_cpp(data, dtype, device, requires_grad, pin_memory)`: Lantern is not loaded. Please use `install_torch()` to install additional dependencies. Backtrace: ▆ 1. └─deepregression::deepregression(...) at test_deepregression_torch.R:181:3 2. └─base::lapply(...) 3. └─deepregression (local) FUN(X[[i]], ...) 4. └─subnetwork_builder[[i]](...) 5. ├─base::do.call(...) 6. └─deepregression (local) ``(...) 7. └─torch::torch_tensor(P) 8. └─Tensor$new(data, dtype, device, requires_grad, pin_memory) 9. └─methods$initialize(NULL, NULL, ...) 10. └─torch:::torch_tensor_cpp(...) ── Error ('test_deepregression_torch.R:220:3'): GAMs with fixed weights ──────── Error in `torch_tensor_cpp(data, dtype, device, requires_grad, pin_memory)`: Lantern is not loaded. Please use `install_torch()` to install additional dependencies. Backtrace: ▆ 1. └─deepregression::deepregression(...) at test_deepregression_torch.R:220:3 2. └─base::lapply(...) 3. └─deepregression (local) FUN(X[[i]], ...) 4. └─subnetwork_builder[[i]](...) 5. └─base::lapply(...) 6. └─deepregression (local) FUN(X[[i]], ...) 7. └─pp_lay[[i]]$layer() 8. ├─base::do.call(layer_class, layer_args) 9. └─deepregression (local) ``(...) 10. └─torch::torch_tensor(P) 11. └─Tensor$new(data, dtype, device, requires_grad, pin_memory) 12. └─methods$initialize(NULL, NULL, ...) 13. └─torch:::torch_tensor_cpp(...) ── Error ('test_ensemble_torch.R:13:3'): deep ensemble ───────────────────────── Error in `torch_tensor_cpp(data, dtype, device, requires_grad, pin_memory)`: Lantern is not loaded. Please use `install_torch()` to install additional dependencies. Backtrace: ▆ 1. └─deepregression::deepregression(...) at test_ensemble_torch.R:13:3 2. └─base::lapply(...) 3. └─deepregression (local) FUN(X[[i]], ...) 4. └─subnetwork_builder[[i]](...) 5. └─base::lapply(...) 6. └─deepregression (local) FUN(X[[i]], ...) 7. └─pp_lay[[i]]$layer() 8. ├─base::do.call(layer_class, layer_args) 9. └─deepregression (local) ``(...) 10. └─torch::torch_tensor(P) 11. └─Tensor$new(data, dtype, device, requires_grad, pin_memory) 12. └─methods$initialize(NULL, NULL, ...) 13. └─torch:::torch_tensor_cpp(...) ── Error ('test_ensemble_torch.R:55:3'): reinitializing weights ──────────────── Error in `torch_tensor_cpp(data, dtype, device, requires_grad, pin_memory)`: Lantern is not loaded. Please use `install_torch()` to install additional dependencies. Backtrace: ▆ 1. └─deepregression::deepregression(...) at test_ensemble_torch.R:55:3 2. └─base::lapply(...) 3. └─deepregression (local) FUN(X[[i]], ...) 4. └─subnetwork_builder[[i]](...) 5. └─base::lapply(...) 6. └─deepregression (local) FUN(X[[i]], ...) 7. └─pp_lay[[i]]$layer() 8. ├─base::do.call(layer_class, layer_args) 9. └─deepregression (local) ``(...) 10. └─torch::torch_tensor(P) 11. └─Tensor$new(data, dtype, device, requires_grad, pin_memory) 12. └─methods$initialize(NULL, NULL, ...) 13. └─torch:::torch_tensor_cpp(...) ── Error ('test_families_torch.R:76:7'): torch families can be fitted ────────── Error in `(function (size, options, memory_format) { .Call(`_torch_cpp_torch_namespace_empty_size_IntArrayRef`, size, options, memory_format) })(size = list(1L, 1L), options = list(dtype = NULL, layout = NULL, device = NULL, requires_grad = FALSE), memory_format = NULL)`: Lantern is not loaded. Please use `install_torch()` to install additional dependencies. Backtrace: ▆ 1. └─deepregression::deepregression(...) at test_families_torch.R:76:7 2. └─base::lapply(...) 3. └─deepregression (local) FUN(X[[i]], ...) 4. └─subnetwork_builder[[i]](...) 5. └─base::lapply(...) 6. └─deepregression (local) FUN(X[[i]], ...) 7. └─pp_lay[[i]]$layer() 8. ├─base::do.call(layer_class, layer_args) 9. └─deepregression (local) ``(...) 10. └─torch (local) layer_module(...) 11. └─Module$new(...) 12. └─deepregression (local) initialize(...) 13. ├─torch::nn_parameter(torch_empty(out_features, in_features)) 14. │ └─torch:::is_torch_tensor(x) 15. └─torch::torch_empty(out_features, in_features) 16. ├─base::do.call(.torch_empty, args) 17. └─torch (local) ``(options = ``, size = ``) 18. └─torch:::call_c_function(...) 19. └─torch:::do_call(f, args) 20. ├─base::do.call(fun, args) 21. └─torch (local) ``(size = ``, options = ``, memory_format = NULL) ── Error ('test_layers_torch.R:6:3'): lasso layers ───────────────────────────── Error in `cpp_torch_manual_seed(as.character(seed))`: Lantern is not loaded. Please use `install_torch()` to install additional dependencies. Backtrace: ▆ 1. └─torch::torch_manual_seed(42) at test_layers_torch.R:6:3 2. └─torch:::cpp_torch_manual_seed(as.character(seed)) ── Error ('test_methods_torch.R:18:3'): all methods ──────────────────────────── Error in `(function (size, options, memory_format) { .Call(`_torch_cpp_torch_namespace_empty_size_IntArrayRef`, size, options, memory_format) })(size = list(1L, 1L), options = list(dtype = NULL, layout = NULL, device = NULL, requires_grad = FALSE), memory_format = NULL)`: Lantern is not loaded. Please use `install_torch()` to install additional dependencies. Backtrace: ▆ 1. └─deepregression::deepregression(...) at test_methods_torch.R:18:3 2. └─base::lapply(...) 3. └─deepregression (local) FUN(X[[i]], ...) 4. └─subnetwork_builder[[i]](...) 5. └─base::lapply(...) 6. └─deepregression (local) FUN(X[[i]], ...) 7. └─pp_lay[[i]]$layer() 8. ├─base::do.call(layer_class, layer_args) 9. └─deepregression (local) ``(...) 10. └─torch (local) layer_module(...) 11. └─Module$new(...) 12. └─deepregression (local) initialize(...) 13. ├─torch::nn_parameter(torch_empty(out_features, in_features)) 14. │ └─torch:::is_torch_tensor(x) 15. └─torch::torch_empty(out_features, in_features) 16. ├─base::do.call(.torch_empty, args) 17. └─torch (local) ``(options = ``, size = ``) 18. └─torch:::call_c_function(...) 19. └─torch:::do_call(f, args) 20. ├─base::do.call(fun, args) 21. └─torch (local) ``(size = ``, options = ``, memory_format = NULL) ── Error ('test_reproducibility_torch.R:21:17'): reproducibility ─────────────── Error in `(function (size, options, memory_format) { .Call(`_torch_cpp_torch_namespace_empty_size_IntArrayRef`, size, options, memory_format) })(size = list(64, 1), options = list(dtype = NULL, layout = NULL, device = NULL, requires_grad = FALSE), memory_format = NULL)`: Lantern is not loaded. Please use `install_torch()` to install additional dependencies. Backtrace: ▆ 1. └─deepregression::deepregression(...) at test_reproducibility_torch.R:33:3 2. └─base::lapply(...) 3. └─deepregression (local) FUN(X[[i]], ...) 4. └─subnetwork_builder[[i]](...) 5. └─base::lapply(...) 6. └─deepregression (local) FUN(X[[i]], ...) 7. └─pp_lay[[i]]$layer() 8. ├─torch::nn_sequential(...) at test_reproducibility_torch.R:21:17 9. │ └─Module$new(...) 10. │ └─torch (local) initialize(...) 11. │ └─rlang::list2(...) 12. └─torch::nn_linear(in_features = 1, out_features = 64, bias = F) 13. └─Module$new(...) 14. └─torch (local) initialize(...) 15. ├─torch::nn_parameter(torch_empty(out_features, in_features)) 16. │ └─torch:::is_torch_tensor(x) 17. └─torch::torch_empty(out_features, in_features) 18. ├─base::do.call(.torch_empty, args) 19. └─torch (local) ``(options = ``, size = ``) 20. └─torch:::call_c_function(...) 21. └─torch:::do_call(f, args) 22. ├─base::do.call(fun, args) 23. └─torch (local) ``(size = ``, options = ``, memory_format = NULL) ── Error ('test_subnetwork_init_torch.R:15:33'): subnetwork_init ─────────────── Error in `(function (size, options, memory_format) { .Call(`_torch_cpp_torch_namespace_empty_size_IntArrayRef`, size, options, memory_format) })(size = list(5, 1), options = list(dtype = NULL, layout = NULL, device = NULL, requires_grad = FALSE), memory_format = NULL)`: Lantern is not loaded. Please use `install_torch()` to install additional dependencies. Backtrace: ▆ 1. └─deepregression::subnetwork_init_torch(list(pp)) at test_subnetwork_init_torch.R:38:3 2. └─base::lapply(...) 3. └─deepregression (local) FUN(X[[i]], ...) 4. └─pp_lay[[i]]$layer() 5. ├─torch::nn_sequential(...) at test_subnetwork_init_torch.R:15:33 6. │ └─Module$new(...) 7. │ └─torch (local) initialize(...) 8. │ └─rlang::list2(...) 9. └─torch::nn_linear(in_features = 1, out_features = 5) 10. └─Module$new(...) 11. └─torch (local) initialize(...) 12. ├─torch::nn_parameter(torch_empty(out_features, in_features)) 13. │ └─torch:::is_torch_tensor(x) 14. └─torch::torch_empty(out_features, in_features) 15. ├─base::do.call(.torch_empty, args) 16. └─torch (local) ``(options = ``, size = ``) 17. └─torch:::call_c_function(...) 18. └─torch:::do_call(f, args) 19. ├─base::do.call(fun, args) 20. └─torch (local) ``(size = ``, options = ``, memory_format = NULL) [ FAIL 14 | WARN 0 | SKIP 1 | PASS 680 ] Error: ! Test failures. Execution halted Package: difNLR Check: tests New result: ERROR Running ‘testthat.R’ [219s/220s] Running the tests in ‘tests/testthat.R’ failed. Complete output: > # This file is part of the standard setup for testthat. > # It is recommended that you do not modify it. > # > # Where should you do additional test configuration? > # Learn more about the roles of various files in: > # * https://r-pkgs.org/testing-design.html#sec-tests-files-overview > # * https://testthat.r-lib.org/articles/special-files.html > > library(testthat) > library(difNLR) > > test_check("difNLR") Saving _problems/test-difNLR-14.R Saving _problems/test-difNLR-27.R Saving _problems/test-difNLR-30.R Saving _problems/test-difNLR-33.R Saving _problems/test-difNLR-67.R Saving _problems/test-difNLR-74.R Saving _problems/test-difNLR-82.R Saving _problems/test-difNLR-90.R Saving _problems/test-difNLR-98.R Saving _problems/test-difNLR-105.R Saving _problems/test-difNLR-112.R Saving _problems/test-difNLR-118.R Saving _problems/test-difNLR-125.R Saving _problems/test-difNLR-134.R Saving _problems/test-difNLR-142.R Saving _problems/test-difNLR-377.R $Item6H_6 Trying to recalculate starting values based on bootstrapped samples... The recalculation of starting values was successful. Saving _problems/test-difNLR-627.R $Item5 $Item5 $Item5 $Item5 Saving _problems/test-difORD-14.R Saving _problems/test-difORD-49.R Saving _problems/test-difORD-55.R Saving _problems/test-difORD-61.R Saving _problems/test-difORD-67.R Saving _problems/test-difORD-73.R Saving _problems/test-estimNLR-96.R [ FAIL 24 | WARN 1 | SKIP 16 | PASS 318 ] ══ Skipped tests (16) ══════════════════════════════════════════════════════════ • On CRAN (16): 'test-ddfMLR.R:1:1', 'test-ddfMLR.R:178:1', 'test-ddfMLR.R:209:1', 'test-difNLR.R:1:1', 'test-difNLR.R:306:1', 'test-difNLR.R:323:1', 'test-difNLR.R:534:1', 'test-difNLR.R:577:1', 'test-difORD.R:1:1', 'test-difORD.R:198:1', 'test-difORD.R:238:1', 'test-estimNLR.R:1:1', 'test-genNLR.R:1:1', 'test-genNLR.R:97:1', 'test-startNLR.R:1:1', 'test-startNLR.R:59:1' ══ Failed tests ════════════════════════════════════════════════════════════════ ── Failure ('test-difNLR.R:14:3'): difNLR - examples at help page ────────────── Expected `fit1` to equal `fit1_expected`. Differences: actual$nlrPAR[[8]] | expected$nlrPAR[[8]] [1] 0.126421690 - 0.126421840 [1] [2] 0.821916864 - 0.821916793 [2] [3] 0.000000000 | 0.000000000 [3] [4] 0.000000000 | 0.000000000 [4] [5] 0.188560897 - 0.188560839 [5] actual$nlrPAR[[18]] | expected$nlrPAR[[18]] [1] -0.534038569 - -0.534038548 [1] [2] 1.013003039 - 1.013003027 [2] [3] 0.000000000 | 0.000000000 [3] [4] 0.000000000 | 0.000000000 [4] [5] 0.029234301 - 0.029234295 [5] actual$nlrSE[[8]] | expected$nlrSE[[8]] [1] 0.343040215 - 0.343040109 [1] [2] 0.169509293 - 0.169509153 [2] [3] 0.000000000 | 0.000000000 [3] [4] 0.000000000 | 0.000000000 [4] [5] 0.131168793 - 0.131168755 [5] actual$nlrSE[[18]] | expected$nlrSE[[18]] [1] 0.211057550 - 0.211057621 [1] [2] 0.135836648 - 0.135836674 [2] [3] 0.000000000 | 0.000000000 [3] [4] 0.000000000 | 0.000000000 [4] [5] 0.061792800 - 0.061792815 [5] `actual$parM0$Item2`: -0.03058685 1.06428058 0.15099716 `expected$parM0$Item2`: -0.03058735 1.06428087 0.15099733 `actual$parM0$Item8`: 0.126421690 0.821916864 0.188560897 `expected$parM0$Item8`: 0.126421840 0.821916793 0.188560839 `actual$parM0$Item18`: -0.534038569 1.013003039 0.029234301 `expected$parM0$Item18`: -0.534038548 1.013003027 0.029234295 `actual$seM0$Item2`: 0.227735222 0.159226517 0.078461058 `expected$seM0$Item2`: 0.227735124 0.159226423 0.078460978 `actual$seM0$Item8`: 0.343040215 0.169509293 0.131168793 `expected$seM0$Item8`: 0.343040109 0.169509153 0.131168755 `actual$seM0$Item18`: 0.211057550 0.135836648 0.061792800 `expected$seM0$Item18`: 0.211057621 0.135836674 0.061792815 actual$covM0$Item2 vs expected$covM0$Item2 [,1] [,2] [,3] - actual$covM0$Item2[1, ] 0.05186333 -0.03021743 -0.017273062 + expected$covM0$Item2[1, ] 0.05186329 -0.03021739 -0.017273036 - actual$covM0$Item2[2, ] -0.03021743 0.02535308 0.010717985 + expected$covM0$Item2[2, ] -0.03021739 0.02535305 0.010717964 - actual$covM0$Item2[3, ] -0.01727306 0.01071799 0.006156138 + expected$covM0$Item2[3, ] -0.01727304 0.01071796 0.006156125 actual$covM0$Item8 vs expected$covM0$Item8 [,1] [,2] [,3] - actual$covM0$Item8[1, ] 0.11767659 -0.05143602 -0.04434402 + expected$covM0$Item8[1, ] 0.11767652 -0.05143595 -0.04434399 - actual$covM0$Item8[2, ] -0.05143602 0.02873340 0.02007202 + expected$covM0$Item8[2, ] -0.05143595 0.02873335 0.02007199 - actual$covM0$Item8[3, ] -0.04434402 0.02007202 0.01720525 + expected$covM0$Item8[3, ] -0.04434399 0.02007199 0.01720524 actual$covM0$Item12 vs expected$covM0$Item12 [,1] [,2] [,3] actual$covM0$Item12[1, ] 0.09424373 -0.04727163 -0.025621089 - actual$covM0$Item12[2, ] -0.04727163 0.03036815 0.013120782 + expected$covM0$Item12[2, ] -0.04727163 0.03036815 0.013120783 actual$covM0$Item12[3, ] -0.02562109 0.01312078 0.007285343 actual$covM0$Item18 vs expected$covM0$Item18 [,1] [,2] [,3] - actual$covM0$Item18[1, ] 0.04454529 -0.025026748 -0.012609287 + expected$covM0$Item18[1, ] 0.04454532 -0.025026763 -0.012609295 - actual$covM0$Item18[2, ] -0.02502675 0.018451595 0.007195376 + expected$covM0$Item18[2, ] -0.02502676 0.018451602 0.007195379 - actual$covM0$Item18[3, ] -0.01260929 0.007195376 0.003818350 + expected$covM0$Item18[3, ] -0.01260929 0.007195379 0.003818352 actual$parM1$Item16 | expected$parM1$Item16 [1] -0.508151611 - -0.508151559 [1] [2] 1.100651976 - 1.100651988 [2] [3] 0.029151096 - 0.029150994 [3] [4] -0.052590851 - -0.052590882 [4] [5] 0.073900244 - 0.073900243 [5] actual$seM1$Item4 | expected$seM1$Item4 [1] 0.897540624 - 0.897540525 [1] [2] 0.181507894 - 0.181507878 [2] [3] 0.137200404 - 0.137200380 [3] [4] 0.123750967 - 0.123750957 [4] [5] 0.683551339 - 0.683551268 [5] actual$seM1$Item15 | expected$seM1$Item15 [1] 0.479733456 - 0.479733216 [1] [2] 0.176905109 - 0.176905028 [2] [3] 0.096186780 - 0.096186799 [3] [4] 0.110288350 - 0.110288371 [4] [5] 0.226535938 - 0.226535796 [5] actual$seM1$Item16 | expected$seM1$Item16 [1] 0.220720963 - 0.220720949 [1] [2] 0.168408323 - 0.168408322 [2] [3] 0.114789154 - 0.114789215 [3] [4] 0.154145713 - 0.154145671 [4] [5] 0.059226682 - 0.059226681 [5] actual$covM1$Item4 vs expected$covM1$Item4 [,1] [,2] [,3] [,4] [,5] - actual$covM1$Item4[1, ] 0.80557917 -0.142048112 -0.048403540 -0.020625834 -0.61089172 + expected$covM1$Item4[1, ] 0.80557899 -0.142048080 -0.048403512 -0.020625812 -0.61089159 - actual$covM1$Item4[2, ] -0.14204811 0.032945116 0.004242145 -0.003338457 0.11118230 + expected$covM1$Item4[2, ] -0.14204808 0.032945110 0.004242140 -0.003338460 0.11118227 - actual$covM1$Item4[3, ] -0.04840354 0.004242145 0.018823951 0.009679530 0.03176359 + expected$covM1$Item4[3, ] -0.04840351 0.004242140 0.018823944 0.009679526 0.03176357 - actual$covM1$Item4[4, ] -0.02062583 -0.003338457 0.009679530 0.015314302 0.01323938 + expected$covM1$Item4[4, ] -0.02062581 -0.003338460 0.009679526 0.015314299 0.01323936 - actual$covM1$Item4[5, ] -0.61089172 0.111182295 0.031763587 0.013239378 0.46724243 + expected$covM1$Item4[5, ] -0.61089159 0.111182271 0.031763567 0.013239362 0.46724234 actual$covM1$Item15 vs expected$covM1$Item15 [,1] [,2] [,3] [,4] [,5] - actual$covM1$Item15[1, ] 0.23014419 -0.075673598 -0.0164225626 0.0142602206 -0.107645624 + expected$covM1$Item15[1, ] 0.23014396 -0.075673516 -0.0164225838 0.0142602480 -0.107645501 - actual$covM1$Item15[2, ] -0.07567360 0.031295417 0.0039230077 -0.0106777284 0.036138771 + expected$covM1$Item15[2, ] -0.07567352 0.031295389 0.0039230145 -0.0106777372 0.036138728 - actual$covM1$Item15[3, ] -0.01642256 0.003923008 0.0092518967 -0.0003337938 0.005756856 + expected$covM1$Item15[3, ] -0.01642258 0.003923014 0.0092519002 -0.0003337978 0.005756865 - actual$covM1$Item15[4, ] 0.01426022 -0.010677728 -0.0003337938 0.0121635202 -0.006860811 + expected$covM1$Item15[4, ] 0.01426025 -0.010677737 -0.0003337978 0.0121635248 -0.006860823 - actual$covM1$Item15[5, ] -0.10764562 0.036138771 0.0057568560 -0.0068608112 0.051318531 + expected$covM1$Item15[5, ] -0.10764550 0.036138728 0.0057568649 -0.0068608232 0.051318467 actual$covM1$Item16 vs expected$covM1$Item16 [,1] [,2] [,3] [,4] [,5] - actual$covM1$Item16[1, ] 0.048717744 -0.029002734 -0.0078734670 0.0048213840 -0.0121378318 + expected$covM1$Item16[1, ] 0.048717737 -0.029002731 -0.0078734692 0.0048213806 -0.0121378308 - actual$covM1$Item16[2, ] -0.029002734 0.028361363 0.0037418245 -0.0134059539 0.0075068703 + expected$covM1$Item16[2, ] -0.029002731 0.028361363 0.0037418255 -0.0134059510 0.0075068701 - actual$covM1$Item16[3, ] -0.007873467 0.003741824 0.0131765499 -0.0057301894 0.0003339497 + expected$covM1$Item16[3, ] -0.007873469 0.003741825 0.0131765640 -0.0057301861 0.0003339493 - actual$covM1$Item16[4, ] 0.004821384 -0.013405954 -0.0057301894 0.0237609010 -0.0005185267 + expected$covM1$Item16[4, ] 0.004821381 -0.013405951 -0.0057301861 0.0237608880 -0.0005185269 - actual$covM1$Item16[5, ] -0.012137832 0.007506870 0.0003339497 -0.0005185267 0.0035077998 + expected$covM1$Item16[5, ] -0.012137831 0.007506870 0.0003339493 -0.0005185269 0.0035077997 ── Failure ('test-difNLR.R:27:3'): difNLR - examples at help page ────────────── Expected `coef(fit1)` to equal `fit1_coef1_expected`. Differences: actual$Item8 vs expected$Item8 [,1] [,2] [,3] - actual$Item8[1, ] 0.8219169 -0.1538132 0.18856090 + expected$Item8[1, ] 0.8219168 -0.1538134 0.18856084 - actual$Item8[2, ] 0.4896848 -1.0273139 -0.06852521 + expected$Item8[2, ] 0.4896850 -1.0273139 -0.06852520 - actual$Item8[3, ] 1.1541490 0.7196874 0.44564701 + expected$Item8[3, ] 1.1541486 0.7196870 0.44564688 actual$Item18 vs expected$Item18 [,1] [,2] [,3] actual$Item18[1, ] 1.0130030 0.5271836 0.02923430 - actual$Item18[2, ] 0.7467681 0.2319369 -0.09187736 + expected$Item18[2, ] 0.7467680 0.2319368 -0.09187740 - actual$Item18[3, ] 1.2792380 0.8224302 0.15034596 + expected$Item18[3, ] 1.2792380 0.8224303 0.15034599 ── Failure ('test-difNLR.R:30:3'): difNLR - examples at help page ────────────── Expected `coef(fit1, SE = TRUE)` to equal `fit1_coef2_expected`. Differences: actual$Item8 vs expected$Item8 [,1] [,2] [,3] - actual$Item8[1, ] 0.8219169 -0.1538132 0.18856090 + expected$Item8[1, ] 0.8219168 -0.1538134 0.18856084 - actual$Item8[2, ] 0.1695093 0.4456718 0.13116879 + expected$Item8[2, ] 0.1695092 0.4456717 0.13116876 - actual$Item8[3, ] 0.4896848 -1.0273139 -0.06852521 + expected$Item8[3, ] 0.4896850 -1.0273139 -0.06852520 - actual$Item8[4, ] 1.1541490 0.7196874 0.44564701 + expected$Item8[4, ] 1.1541486 0.7196870 0.44564688 actual$Item18 vs expected$Item18 [,1] [,2] [,3] actual$Item18[1, ] 1.0130030 0.5271836 0.02923430 - actual$Item18[2, ] 0.1358366 0.1506388 0.06179280 + expected$Item18[2, ] 0.1358367 0.1506389 0.06179281 - actual$Item18[3, ] 0.7467681 0.2319369 -0.09187736 + expected$Item18[3, ] 0.7467680 0.2319368 -0.09187740 - actual$Item18[4, ] 1.2792380 0.8224302 0.15034596 + expected$Item18[4, ] 1.2792380 0.8224303 0.15034599 ── Failure ('test-difNLR.R:33:3'): difNLR - examples at help page ────────────── Expected `coef(fit1, SE = TRUE, simplify = TRUE)` to equal `fit1_coef3_expected`. Differences: actual vs expected a b c actual[26, ] 0.1809697 0.83289393 0.27959452 actual[27, ] 0.3651398 -2.40962136 -0.54799520 actual[28, ] 1.0745281 0.85526284 0.54799520 - actual[29, ] 0.8219169 -0.15381323 0.18856090 + expected[29, ] 0.8219168 -0.15381343 0.18856084 - actual[30, ] 0.1695093 0.44567177 0.13116879 + expected[30, ] 0.1695092 0.44567169 0.13116876 - actual[31, ] 0.4896848 -1.02731386 -0.06852521 + expected[31, ] 0.4896850 -1.02731389 -0.06852520 - actual[32, ] 1.1541490 0.71968739 0.44564701 + expected[32, ] 1.1541486 0.71968703 0.44564688 actual[33, ] 0.6387164 -0.38314116 0.05162489 actual[34, ] 0.1603240 0.81712635 0.22952065 actual[35, ] 0.3244872 -1.98467937 -0.39822733 actual vs expected a b c actual[44, ] 1.1520042 0.06037568 0.41152338 actual[45, ] 0.8977892 0.38216524 0.20920692 actual[46, ] 0.1742646 0.27856840 0.08535422 - actual[47, ] 0.5562369 -0.16381879 0.04191573 + expected[47, ] 0.5562369 -0.16381880 0.04191572 actual[48, ] 1.2393415 0.92814928 0.37649812 actual[49, ] 0.8157721 -0.77457288 0.29437966 actual[50, ] 0.1918456 0.69590763 0.18393642 actual vs expected a b c actual[66, ] 0.1669037 0.40461315 0.11558571 actual[67, ] 0.3576462 -0.03285713 -0.11700521 actual[68, ] 1.0118965 1.55319727 0.33608245 - actual[69, ] 1.0130030 0.52718358 0.02923430 + expected[69, ] 1.0130030 0.52718357 0.02923430 - actual[70, ] 0.1358366 0.15063882 0.06179280 + expected[70, ] 0.1358367 0.15063888 0.06179281 - actual[71, ] 0.7467681 0.23193692 -0.09187736 + expected[71, ] 0.7467680 0.23193679 -0.09187740 - actual[72, ] 1.2792380 0.82243024 0.15034596 + expected[72, ] 1.2792380 0.82243034 0.15034599 actual[73, ] 1.0724564 0.32683925 0.06295113 actual[74, ] 0.1856114 0.19769855 0.07991250 actual[75, ] 0.7086647 -0.06064279 -0.09367449 actual$a | expected$a [26] 0.180969726 | 0.180969726 [26] [27] 0.365139778 | 0.365139778 [27] [28] 1.074528068 | 1.074528068 [28] [29] 0.821916864 - 0.821916793 [29] [30] 0.169509293 - 0.169509153 [30] [31] 0.489684755 - 0.489684958 [31] [32] 1.154148973 - 1.154148629 [32] [33] 0.638716386 | 0.638716386 [33] [34] 0.160323968 | 0.160323968 [34] [35] 0.324487183 | 0.324487183 [35] actual$a | expected$a [43] 0.574045716 | 0.574045716 [43] [44] 1.152004161 | 1.152004161 [44] [45] 0.897789207 | 0.897789207 [45] [46] 0.174264594 - 0.174264595 [46] [47] 0.556236880 - 0.556236876 [47] [48] 1.239341534 - 1.239341538 [48] [49] 0.815772114 | 0.815772114 [49] [50] 0.191845634 | 0.191845634 [50] [51] 0.439761580 | 0.439761580 [51] actual$a | expected$a [66] 0.166903664 | 0.166903664 [66] [67] 0.357646196 | 0.357646196 [67] [68] 1.011896538 | 1.011896538 [68] [69] 1.013003039 - 1.013003027 [69] [70] 0.135836648 - 0.135836674 [70] [71] 0.746768101 - 0.746768039 [71] [72] 1.279237976 - 1.279238015 [72] [73] 1.072456380 | 1.072456380 [73] [74] 0.185611422 | 0.185611422 [74] [75] 0.708664678 | 0.708664678 [75] actual$b | expected$b [26] 0.832893927 | 0.832893927 [26] [27] -2.409621359 | -2.409621359 [27] [28] 0.855262841 | 0.855262841 [28] [29] -0.153813233 - -0.153813428 [29] [30] 0.445671773 - 0.445671689 [30] [31] -1.027313856 - -1.027313887 [31] [32] 0.719687391 - 0.719687031 [32] [33] -0.383141158 | -0.383141158 [33] [34] 0.817126345 | 0.817126345 [34] [35] -1.984679366 | -1.984679366 [35] actual$b | expected$b [43] -1.960728585 | -1.960728585 [43] [44] 0.060375684 | 0.060375684 [44] [45] 0.382165243 | 0.382165243 [45] [46] 0.278568401 - 0.278568404 [46] [47] -0.163818790 - -0.163818797 [47] [48] 0.928149276 - 0.928149283 [48] [49] -0.774572883 | -0.774572883 [49] [50] 0.695907633 | 0.695907633 [50] [51] -2.138526780 | -2.138526780 [51] actual$b | expected$b [66] 0.404613149 | 0.404613149 [66] [67] -0.032857125 | -0.032857125 [67] [68] 1.553197273 | 1.553197273 [68] [69] 0.527183580 - 0.527183565 [69] [70] 0.150638819 - 0.150638876 [70] [71] 0.231936920 - 0.231936795 [71] [72] 0.822430240 - 0.822430336 [72] [73] 0.326839252 | 0.326839252 [73] [74] 0.197698550 | 0.197698550 [74] [75] -0.060642785 | -0.060642785 [75] actual$c | expected$c [26] 0.279594525 | 0.279594525 [26] [27] -0.547995199 | -0.547995199 [27] [28] 0.547995199 | 0.547995199 [28] [29] 0.188560897 - 0.188560839 [29] [30] 0.131168793 - 0.131168755 [30] [31] -0.068525213 - -0.068525197 [31] [32] 0.445647007 - 0.445646875 [32] [33] 0.051624886 | 0.051624886 [33] [34] 0.229520652 | 0.229520652 [34] [35] -0.398227326 | -0.398227326 [35] actual$c | expected$c [43] -0.385549911 | -0.385549911 [43] [44] 0.411523383 | 0.411523383 [44] [45] 0.209206920 | 0.209206920 [45] [46] 0.085354219 - 0.085354220 [46] [47] 0.041915725 - 0.041915723 [47] [48] 0.376498115 - 0.376498117 [48] [49] 0.294379661 | 0.294379661 [49] [50] 0.183936421 | 0.183936421 [50] [51] -0.066129098 | -0.066129098 [51] actual$c | expected$c [66] 0.115585709 | 0.115585709 [66] [67] -0.117005207 | -0.117005207 [67] [68] 0.336082448 | 0.336082448 [68] [69] 0.029234301 - 0.029234295 [69] [70] 0.061792800 - 0.061792815 [70] [71] -0.091877362 - -0.091877396 [71] [72] 0.150345965 - 0.150345987 [72] [73] 0.062951126 | 0.062951126 [73] [74] 0.079912497 | 0.079912497 [74] [75] -0.093674490 | -0.093674490 [75] ── Failure ('test-difNLR.R:67:3'): difNLR - examples at help page ────────────── Expected `fit2` to equal `fit2_expected`. Differences: actual$Sval | expected$Sval [1] 41.91446590 | 41.91446590 [1] [2] 14.73970661 | 14.73970661 [2] [3] 0.69033084 | 0.69033084 [3] [4] 2.86221711 - 2.86221791 [4] [5] 1.08904070 | 1.08904070 [5] [6] 0.15466706 | 0.15466706 [6] [7] 5.68978837 | 5.68978837 [7] actual$Sval | expected$Sval [12] 1.01430708 | 1.01430708 [12] [13] 3.85490451 | 3.85490451 [13] [14] 1.41492671 | 1.41492671 [14] [15] 1.10837744 - 1.10837665 [15] [16] 0.13939499 - 0.13939486 [16] [17] 2.56488158 | 2.56488158 [17] [18] 1.94129713 | 1.94129713 [18] [19] 4.80670067 | 4.80670067 [19] actual$nlrPAR[[8]] | expected$nlrPAR[[8]] [1] 0.126421690 - 0.126421840 [1] [2] 0.821916864 - 0.821916793 [2] [3] 0.000000000 | 0.000000000 [3] [4] 0.000000000 | 0.000000000 [4] [5] 0.188560897 - 0.188560839 [5] actual$nlrPAR[[18]] | expected$nlrPAR[[18]] [1] -0.534038569 - -0.534038548 [1] [2] 1.013003039 - 1.013003027 [2] [3] 0.000000000 | 0.000000000 [3] [4] 0.000000000 | 0.000000000 [4] [5] 0.029234301 - 0.029234295 [5] actual$nlrSE[[8]] | expected$nlrSE[[8]] [1] 0.343040215 - 0.343040109 [1] [2] 0.169509293 - 0.169509153 [2] [3] 0.000000000 | 0.000000000 [3] [4] 0.000000000 | 0.000000000 [4] [5] 0.131168793 - 0.131168755 [5] actual$nlrSE[[18]] | expected$nlrSE[[18]] [1] 0.211057550 - 0.211057621 [1] [2] 0.135836648 - 0.135836674 [2] [3] 0.000000000 | 0.000000000 [3] [4] 0.000000000 | 0.000000000 [4] [5] 0.061792800 - 0.061792815 [5] `actual$parM0$Item2`: -0.03058685 1.06428058 0.15099716 `expected$parM0$Item2`: -0.03058735 1.06428087 0.15099733 `actual$parM0$Item8`: 0.126421690 0.821916864 0.188560897 `expected$parM0$Item8`: 0.126421840 0.821916793 0.188560839 `actual$parM0$Item18`: -0.534038569 1.013003039 0.029234301 `expected$parM0$Item18`: -0.534038548 1.013003027 0.029234295 `actual$seM0$Item2`: 0.227735222 0.159226517 0.078461058 `expected$seM0$Item2`: 0.227735124 0.159226423 0.078460978 `actual$seM0$Item8`: 0.343040215 0.169509293 0.131168793 `expected$seM0$Item8`: 0.343040109 0.169509153 0.131168755 `actual$seM0$Item18`: 0.211057550 0.135836648 0.061792800 `expected$seM0$Item18`: 0.211057621 0.135836674 0.061792815 actual$covM0$Item2 vs expected$covM0$Item2 [,1] [,2] [,3] - actual$covM0$Item2[1, ] 0.05186333 -0.03021743 -0.017273062 + expected$covM0$Item2[1, ] 0.05186329 -0.03021739 -0.017273036 - actual$covM0$Item2[2, ] -0.03021743 0.02535308 0.010717985 + expected$covM0$Item2[2, ] -0.03021739 0.02535305 0.010717964 - actual$covM0$Item2[3, ] -0.01727306 0.01071799 0.006156138 + expected$covM0$Item2[3, ] -0.01727304 0.01071796 0.006156125 actual$covM0$Item8 vs expected$covM0$Item8 [,1] [,2] [,3] - actual$covM0$Item8[1, ] 0.11767659 -0.05143602 -0.04434402 + expected$covM0$Item8[1, ] 0.11767652 -0.05143595 -0.04434399 - actual$covM0$Item8[2, ] -0.05143602 0.02873340 0.02007202 + expected$covM0$Item8[2, ] -0.05143595 0.02873335 0.02007199 - actual$covM0$Item8[3, ] -0.04434402 0.02007202 0.01720525 + expected$covM0$Item8[3, ] -0.04434399 0.02007199 0.01720524 actual$covM0$Item12 vs expected$covM0$Item12 [,1] [,2] [,3] actual$covM0$Item12[1, ] 0.09424373 -0.04727163 -0.025621089 - actual$covM0$Item12[2, ] -0.04727163 0.03036815 0.013120782 + expected$covM0$Item12[2, ] -0.04727163 0.03036815 0.013120783 actual$covM0$Item12[3, ] -0.02562109 0.01312078 0.007285343 actual$covM0$Item18 vs expected$covM0$Item18 [,1] [,2] [,3] - actual$covM0$Item18[1, ] 0.04454529 -0.025026748 -0.012609287 + expected$covM0$Item18[1, ] 0.04454532 -0.025026763 -0.012609295 - actual$covM0$Item18[2, ] -0.02502675 0.018451595 0.007195376 + expected$covM0$Item18[2, ] -0.02502676 0.018451602 0.007195379 - actual$covM0$Item18[3, ] -0.01260929 0.007195376 0.003818350 + expected$covM0$Item18[3, ] -0.01260929 0.007195379 0.003818352 actual$parM1$Item16 | expected$parM1$Item16 [1] -0.508151611 - -0.508151559 [1] [2] 1.100651976 - 1.100651988 [2] [3] 0.029151096 - 0.029150994 [3] [4] -0.052590851 - -0.052590882 [4] [5] 0.073900244 - 0.073900243 [5] actual$seM1$Item4 | expected$seM1$Item4 [1] 0.897540624 - 0.897540525 [1] [2] 0.181507894 - 0.181507878 [2] [3] 0.137200404 - 0.137200380 [3] [4] 0.123750967 - 0.123750957 [4] [5] 0.683551339 - 0.683551268 [5] actual$seM1$Item15 | expected$seM1$Item15 [1] 0.479733456 - 0.479733216 [1] [2] 0.176905109 - 0.176905028 [2] [3] 0.096186780 - 0.096186799 [3] [4] 0.110288350 - 0.110288371 [4] [5] 0.226535938 - 0.226535796 [5] actual$seM1$Item16 | expected$seM1$Item16 [1] 0.220720963 - 0.220720949 [1] [2] 0.168408323 - 0.168408322 [2] [3] 0.114789154 - 0.114789215 [3] [4] 0.154145713 - 0.154145671 [4] [5] 0.059226682 - 0.059226681 [5] actual$covM1$Item4 vs expected$covM1$Item4 [,1] [,2] [,3] [,4] [,5] - actual$covM1$Item4[1, ] 0.80557917 -0.142048112 -0.048403540 -0.020625834 -0.61089172 + expected$covM1$Item4[1, ] 0.80557899 -0.142048080 -0.048403512 -0.020625812 -0.61089159 - actual$covM1$Item4[2, ] -0.14204811 0.032945116 0.004242145 -0.003338457 0.11118230 + expected$covM1$Item4[2, ] -0.14204808 0.032945110 0.004242140 -0.003338460 0.11118227 - actual$covM1$Item4[3, ] -0.04840354 0.004242145 0.018823951 0.009679530 0.03176359 + expected$covM1$Item4[3, ] -0.04840351 0.004242140 0.018823944 0.009679526 0.03176357 - actual$covM1$Item4[4, ] -0.02062583 -0.003338457 0.009679530 0.015314302 0.01323938 + expected$covM1$Item4[4, ] -0.02062581 -0.003338460 0.009679526 0.015314299 0.01323936 - actual$covM1$Item4[5, ] -0.61089172 0.111182295 0.031763587 0.013239378 0.46724243 + expected$covM1$Item4[5, ] -0.61089159 0.111182271 0.031763567 0.013239362 0.46724234 actual$covM1$Item15 vs expected$covM1$Item15 [,1] [,2] [,3] [,4] [,5] - actual$covM1$Item15[1, ] 0.23014419 -0.075673598 -0.0164225626 0.0142602206 -0.107645624 + expected$covM1$Item15[1, ] 0.23014396 -0.075673516 -0.0164225838 0.0142602480 -0.107645501 - actual$covM1$Item15[2, ] -0.07567360 0.031295417 0.0039230077 -0.0106777284 0.036138771 + expected$covM1$Item15[2, ] -0.07567352 0.031295389 0.0039230145 -0.0106777372 0.036138728 - actual$covM1$Item15[3, ] -0.01642256 0.003923008 0.0092518967 -0.0003337938 0.005756856 + expected$covM1$Item15[3, ] -0.01642258 0.003923014 0.0092519002 -0.0003337978 0.005756865 - actual$covM1$Item15[4, ] 0.01426022 -0.010677728 -0.0003337938 0.0121635202 -0.006860811 + expected$covM1$Item15[4, ] 0.01426025 -0.010677737 -0.0003337978 0.0121635248 -0.006860823 - actual$covM1$Item15[5, ] -0.10764562 0.036138771 0.0057568560 -0.0068608112 0.051318531 + expected$covM1$Item15[5, ] -0.10764550 0.036138728 0.0057568649 -0.0068608232 0.051318467 actual$covM1$Item16 vs expected$covM1$Item16 [,1] [,2] [,3] [,4] [,5] - actual$covM1$Item16[1, ] 0.048717744 -0.029002734 -0.0078734670 0.0048213840 -0.0121378318 + expected$covM1$Item16[1, ] 0.048717737 -0.029002731 -0.0078734692 0.0048213806 -0.0121378308 - actual$covM1$Item16[2, ] -0.029002734 0.028361363 0.0037418245 -0.0134059539 0.0075068703 + expected$covM1$Item16[2, ] -0.029002731 0.028361363 0.0037418255 -0.0134059510 0.0075068701 - actual$covM1$Item16[3, ] -0.007873467 0.003741824 0.0131765499 -0.0057301894 0.0003339497 + expected$covM1$Item16[3, ] -0.007873469 0.003741825 0.0131765640 -0.0057301861 0.0003339493 - actual$covM1$Item16[4, ] 0.004821384 -0.013405954 -0.0057301894 0.0237609010 -0.0005185267 + expected$covM1$Item16[4, ] 0.004821381 -0.013405951 -0.0057301861 0.0237608880 -0.0005185269 - actual$covM1$Item16[5, ] -0.012137832 0.007506870 0.0003339497 -0.0005185267 0.0035077998 + expected$covM1$Item16[5, ] -0.012137831 0.007506870 0.0003339493 -0.0005185269 0.0035077997 actual$pval | expected$pval [1] 0.000000001 | 0.000000001 [1] [2] 0.000629961 | 0.000629961 [2] [3] 0.708103210 | 0.708103210 [3] [4] 0.239043782 - 0.239043686 [4] [5] 0.580119972 | 0.580119972 [5] [6] 0.925581095 | 0.925581095 [6] [7] 0.058140418 | 0.058140418 [7] actual$pval | expected$pval [12] 0.602207300 | 0.602207300 [12] [13] 0.145518471 | 0.145518471 [13] [14] 0.492892908 | 0.492892908 [14] [15] 0.574538182 - 0.574538409 [15] [16] 0.932675918 - 0.932675979 [16] [17] 0.277359497 | 0.277359497 [17] [18] 0.378837258 | 0.378837258 [18] [19] 0.090414527 | 0.090414527 [19] actual$adj.pval | expected$adj.pval [1] 0.000000001 | 0.000000001 [1] [2] 0.000629961 | 0.000629961 [2] [3] 0.708103210 | 0.708103210 [3] [4] 0.239043782 - 0.239043686 [4] [5] 0.580119972 | 0.580119972 [5] [6] 0.925581095 | 0.925581095 [6] [7] 0.058140418 | 0.058140418 [7] actual$adj.pval | expected$adj.pval [12] 0.602207300 | 0.602207300 [12] [13] 0.145518471 | 0.145518471 [13] [14] 0.492892908 | 0.492892908 [14] [15] 0.574538182 - 0.574538409 [15] [16] 0.932675918 - 0.932675979 [16] [17] 0.277359497 | 0.277359497 [17] [18] 0.378837258 | 0.378837258 [18] [19] 0.090414527 | 0.090414527 [19] ── Failure ('test-difNLR.R:74:3'): difNLR - examples at help page ────────────── Expected `fit3` to equal `fit3_expected`. Differences: actual$nlrPAR[[8]] | expected$nlrPAR[[8]] [1] 0.126421690 - 0.126421840 [1] [2] 0.821916864 - 0.821916793 [2] [3] 0.000000000 | 0.000000000 [3] [4] 0.000000000 | 0.000000000 [4] [5] 0.188560897 - 0.188560839 [5] actual$nlrPAR[[18]] | expected$nlrPAR[[18]] [1] -0.534038569 - -0.534038548 [1] [2] 1.013003039 - 1.013003027 [2] [3] 0.000000000 | 0.000000000 [3] [4] 0.000000000 | 0.000000000 [4] [5] 0.029234301 - 0.029234295 [5] actual$nlrSE[[8]] | expected$nlrSE[[8]] [1] 0.343040215 - 0.343040109 [1] [2] 0.169509293 - 0.169509153 [2] [3] 0.000000000 | 0.000000000 [3] [4] 0.000000000 | 0.000000000 [4] [5] 0.131168793 - 0.131168755 [5] actual$nlrSE[[18]] | expected$nlrSE[[18]] [1] 0.211057550 - 0.211057621 [1] [2] 0.135836648 - 0.135836674 [2] [3] 0.000000000 | 0.000000000 [3] [4] 0.000000000 | 0.000000000 [4] [5] 0.061792800 - 0.061792815 [5] `actual$parM0$Item2`: -0.03058685 1.06428058 0.15099716 `expected$parM0$Item2`: -0.03058735 1.06428087 0.15099733 `actual$parM0$Item8`: 0.126421690 0.821916864 0.188560897 `expected$parM0$Item8`: 0.126421840 0.821916793 0.188560839 `actual$parM0$Item18`: -0.534038569 1.013003039 0.029234301 `expected$parM0$Item18`: -0.534038548 1.013003027 0.029234295 `actual$seM0$Item2`: 0.227735222 0.159226517 0.078461058 `expected$seM0$Item2`: 0.227735124 0.159226423 0.078460978 `actual$seM0$Item8`: 0.343040215 0.169509293 0.131168793 `expected$seM0$Item8`: 0.343040109 0.169509153 0.131168755 `actual$seM0$Item18`: 0.211057550 0.135836648 0.061792800 `expected$seM0$Item18`: 0.211057621 0.135836674 0.061792815 actual$covM0$Item2 vs expected$covM0$Item2 [,1] [,2] [,3] - actual$covM0$Item2[1, ] 0.05186333 -0.03021743 -0.017273062 + expected$covM0$Item2[1, ] 0.05186329 -0.03021739 -0.017273036 - actual$covM0$Item2[2, ] -0.03021743 0.02535308 0.010717985 + expected$covM0$Item2[2, ] -0.03021739 0.02535305 0.010717964 - actual$covM0$Item2[3, ] -0.01727306 0.01071799 0.006156138 + expected$covM0$Item2[3, ] -0.01727304 0.01071796 0.006156125 actual$covM0$Item8 vs expected$covM0$Item8 [,1] [,2] [,3] - actual$covM0$Item8[1, ] 0.11767659 -0.05143602 -0.04434402 + expected$covM0$Item8[1, ] 0.11767652 -0.05143595 -0.04434399 - actual$covM0$Item8[2, ] -0.05143602 0.02873340 0.02007202 + expected$covM0$Item8[2, ] -0.05143595 0.02873335 0.02007199 - actual$covM0$Item8[3, ] -0.04434402 0.02007202 0.01720525 + expected$covM0$Item8[3, ] -0.04434399 0.02007199 0.01720524 actual$covM0$Item12 vs expected$covM0$Item12 [,1] [,2] [,3] actual$covM0$Item12[1, ] 0.09424373 -0.04727163 -0.025621089 - actual$covM0$Item12[2, ] -0.04727163 0.03036815 0.013120782 + expected$covM0$Item12[2, ] -0.04727163 0.03036815 0.013120783 actual$covM0$Item12[3, ] -0.02562109 0.01312078 0.007285343 actual$covM0$Item18 vs expected$covM0$Item18 [,1] [,2] [,3] - actual$covM0$Item18[1, ] 0.04454529 -0.025026748 -0.012609287 + expected$covM0$Item18[1, ] 0.04454532 -0.025026763 -0.012609295 - actual$covM0$Item18[2, ] -0.02502675 0.018451595 0.007195376 + expected$covM0$Item18[2, ] -0.02502676 0.018451602 0.007195379 - actual$covM0$Item18[3, ] -0.01260929 0.007195376 0.003818350 + expected$covM0$Item18[3, ] -0.01260929 0.007195379 0.003818352 actual$parM1$Item16 | expected$parM1$Item16 [1] -0.508151611 - -0.508151559 [1] [2] 1.100651976 - 1.100651988 [2] [3] 0.029151096 - 0.029150994 [3] [4] -0.052590851 - -0.052590882 [4] [5] 0.073900244 - 0.073900243 [5] actual$seM1$Item4 | expected$seM1$Item4 [1] 0.897540624 - 0.897540525 [1] [2] 0.181507894 - 0.181507878 [2] [3] 0.137200404 - 0.137200380 [3] [4] 0.123750967 - 0.123750957 [4] [5] 0.683551339 - 0.683551268 [5] actual$seM1$Item15 | expected$seM1$Item15 [1] 0.479733456 - 0.479733216 [1] [2] 0.176905109 - 0.176905028 [2] [3] 0.096186780 - 0.096186799 [3] [4] 0.110288350 - 0.110288371 [4] [5] 0.226535938 - 0.226535796 [5] actual$seM1$Item16 | expected$seM1$Item16 [1] 0.220720963 - 0.220720949 [1] [2] 0.168408323 - 0.168408322 [2] [3] 0.114789154 - 0.114789215 [3] [4] 0.154145713 - 0.154145671 [4] [5] 0.059226682 - 0.059226681 [5] actual$covM1$Item4 vs expected$covM1$Item4 [,1] [,2] [,3] [,4] [,5] - actual$covM1$Item4[1, ] 0.80557917 -0.142048112 -0.048403540 -0.020625834 -0.61089172 + expected$covM1$Item4[1, ] 0.80557899 -0.142048080 -0.048403512 -0.020625812 -0.61089159 - actual$covM1$Item4[2, ] -0.14204811 0.032945116 0.004242145 -0.003338457 0.11118230 + expected$covM1$Item4[2, ] -0.14204808 0.032945110 0.004242140 -0.003338460 0.11118227 - actual$covM1$Item4[3, ] -0.04840354 0.004242145 0.018823951 0.009679530 0.03176359 + expected$covM1$Item4[3, ] -0.04840351 0.004242140 0.018823944 0.009679526 0.03176357 - actual$covM1$Item4[4, ] -0.02062583 -0.003338457 0.009679530 0.015314302 0.01323938 + expected$covM1$Item4[4, ] -0.02062581 -0.003338460 0.009679526 0.015314299 0.01323936 - actual$covM1$Item4[5, ] -0.61089172 0.111182295 0.031763587 0.013239378 0.46724243 + expected$covM1$Item4[5, ] -0.61089159 0.111182271 0.031763567 0.013239362 0.46724234 actual$covM1$Item15 vs expected$covM1$Item15 [,1] [,2] [,3] [,4] [,5] - actual$covM1$Item15[1, ] 0.23014419 -0.075673598 -0.0164225626 0.0142602206 -0.107645624 + expected$covM1$Item15[1, ] 0.23014396 -0.075673516 -0.0164225838 0.0142602480 -0.107645501 - actual$covM1$Item15[2, ] -0.07567360 0.031295417 0.0039230077 -0.0106777284 0.036138771 + expected$covM1$Item15[2, ] -0.07567352 0.031295389 0.0039230145 -0.0106777372 0.036138728 - actual$covM1$Item15[3, ] -0.01642256 0.003923008 0.0092518967 -0.0003337938 0.005756856 + expected$covM1$Item15[3, ] -0.01642258 0.003923014 0.0092519002 -0.0003337978 0.005756865 - actual$covM1$Item15[4, ] 0.01426022 -0.010677728 -0.0003337938 0.0121635202 -0.006860811 + expected$covM1$Item15[4, ] 0.01426025 -0.010677737 -0.0003337978 0.0121635248 -0.006860823 - actual$covM1$Item15[5, ] -0.10764562 0.036138771 0.0057568560 -0.0068608112 0.051318531 + expected$covM1$Item15[5, ] -0.10764550 0.036138728 0.0057568649 -0.0068608232 0.051318467 actual$covM1$Item16 vs expected$covM1$Item16 [,1] [,2] [,3] [,4] [,5] - actual$covM1$Item16[1, ] 0.048717744 -0.029002734 -0.0078734670 0.0048213840 -0.0121378318 + expected$covM1$Item16[1, ] 0.048717737 -0.029002731 -0.0078734692 0.0048213806 -0.0121378308 - actual$covM1$Item16[2, ] -0.029002734 0.028361363 0.0037418245 -0.0134059539 0.0075068703 + expected$covM1$Item16[2, ] -0.029002731 0.028361363 0.0037418255 -0.0134059510 0.0075068701 - actual$covM1$Item16[3, ] -0.007873467 0.003741824 0.0131765499 -0.0057301894 0.0003339497 + expected$covM1$Item16[3, ] -0.007873469 0.003741825 0.0131765640 -0.0057301861 0.0003339493 - actual$covM1$Item16[4, ] 0.004821384 -0.013405954 -0.0057301894 0.0237609010 -0.0005185267 + expected$covM1$Item16[4, ] 0.004821381 -0.013405951 -0.0057301861 0.0237608880 -0.0005185269 - actual$covM1$Item16[5, ] -0.012137832 0.007506870 0.0003339497 -0.0005185267 0.0035077998 + expected$covM1$Item16[5, ] -0.012137831 0.007506870 0.0003339493 -0.0005185269 0.0035077997 ── Failure ('test-difNLR.R:82:3'): difNLR - examples at help page ────────────── Expected `fit4` to equal `fit4_expected`. Differences: actual$nlrPAR[[8]] | expected$nlrPAR[[8]] [1] 0.126421690 - 0.126421840 [1] [2] 0.821916864 - 0.821916793 [2] [3] 0.000000000 | 0.000000000 [3] [4] 0.000000000 | 0.000000000 [4] [5] 0.188560897 - 0.188560839 [5] actual$nlrPAR[[18]] | expected$nlrPAR[[18]] [1] -0.534038569 - -0.534038548 [1] [2] 1.013003039 - 1.013003027 [2] [3] 0.000000000 | 0.000000000 [3] [4] 0.000000000 | 0.000000000 [4] [5] 0.029234301 - 0.029234295 [5] actual$nlrSE[[8]] | expected$nlrSE[[8]] [1] 0.401197762 - 0.401197856 [1] [2] 0.178215930 - 0.178215952 [2] [3] 0.000000000 | 0.000000000 [3] [4] 0.000000000 | 0.000000000 [4] [5] 0.149918697 - 0.149918758 [5] `actual$parM0$Item2`: -0.03058685 1.06428058 0.15099716 `expected$parM0$Item2`: -0.03058735 1.06428087 0.15099733 `actual$parM0$Item8`: 0.126421690 0.821916864 0.188560897 `expected$parM0$Item8`: 0.126421840 0.821916793 0.188560839 `actual$parM0$Item18`: -0.534038569 1.013003039 0.029234301 `expected$parM0$Item18`: -0.534038548 1.013003027 0.029234295 `actual$seM0$Item2`: 0.201224594 0.140058483 0.067742972 `expected$seM0$Item2`: 0.201224549 0.140058472 0.067742918 `actual$seM0$Item8`: 0.401197762 0.178215930 0.149918697 `expected$seM0$Item8`: 0.401197856 0.178215952 0.149918758 actual$covM0$Item2 vs expected$covM0$Item2 [,1] [,2] [,3] - actual$covM0$Item2[1, ] 0.04049134 -0.022395787 -0.013017637 + expected$covM0$Item2[1, ] 0.04049132 -0.022395776 -0.013017623 - actual$covM0$Item2[2, ] -0.02239579 0.019616379 0.007736527 + expected$covM0$Item2[2, ] -0.02239578 0.019616376 0.007736518 - actual$covM0$Item2[3, ] -0.01301764 0.007736527 0.004589110 + expected$covM0$Item2[3, ] -0.01301762 0.007736518 0.004589103 actual$covM0$Item8 vs expected$covM0$Item8 [,1] [,2] [,3] - actual$covM0$Item8[1, ] 0.16095964 -0.06601989 -0.05950329 + expected$covM0$Item8[1, ] 0.16095972 -0.06601992 -0.05950333 - actual$covM0$Item8[2, ] -0.06601989 0.03176092 0.02478046 + expected$covM0$Item8[2, ] -0.06601992 0.03176093 0.02478048 - actual$covM0$Item8[3, ] -0.05950329 0.02478046 0.02247562 + expected$covM0$Item8[3, ] -0.05950333 0.02478048 0.02247563 actual$parM1$Item16 | expected$parM1$Item16 [1] -0.508151611 - -0.508151559 [1] [2] 1.100651976 - 1.100651988 [2] [3] 0.029151096 - 0.029150994 [3] [4] -0.052590851 - -0.052590882 [4] [5] 0.073900244 - 0.073900243 [5] actual$seM1$Item15 | expected$seM1$Item15 [1] 0.433862773 - 0.433862784 [1] [2] 0.143168315 - 0.143168318 [2] [3] 0.096666823 | 0.096666823 [3] [4] 0.093469202 | 0.093469202 [4] [5] 0.199418936 - 0.199418941 [5] actual$seM1$Item16 | expected$seM1$Item16 [1] 0.210469737 - 0.210469732 [1] [2] 0.136469436 - 0.136469438 [2] [3] 0.120126749 - 0.120126750 [3] [4] 0.136762974 - 0.136762975 [4] [5] 0.053001686 - 0.053001689 [5] actual$covM1$Item4 vs expected$covM1$Item4 [,1] [,2] [,3] [,4] [,5] - actual$covM1$Item4[1, ] 0.81404561 -0.143623881 -0.036790370 0.012083824 -0.61299622 + expected$covM1$Item4[1, ] 0.81404560 -0.143623886 -0.036790367 0.012083838 -0.61299621 - actual$covM1$Item4[2, ] -0.14362388 0.033532974 0.003260785 -0.010009923 0.11059981 + expected$covM1$Item4[2, ] -0.14362389 0.033532976 0.003260785 -0.010009925 0.11059981 - actual$covM1$Item4[3, ] -0.03679037 0.003260785 0.016150301 0.005334551 0.02288197 + expected$covM1$Item4[3, ] -0.03679037 0.003260785 0.016150301 0.005334550 0.02288197 - actual$covM1$Item4[4, ] 0.01208382 -0.010009923 0.005334551 0.016145291 -0.01091319 + expected$covM1$Item4[4, ] 0.01208384 -0.010009925 0.005334550 0.016145291 -0.01091320 - actual$covM1$Item4[5, ] -0.61299622 0.110599810 0.022881971 -0.010913190 0.46532535 + expected$covM1$Item4[5, ] -0.61299621 0.110599813 0.022881969 -0.010913201 0.46532534 actual$covM1$Item15 vs expected$covM1$Item15 [,1] [,2] [,3] [,4] [,5] - actual$covM1$Item15[1, ] 0.188236906 -0.054552810 -0.0142410655 0.0064748047 -0.085510546 + expected$covM1$Item15[1, ] 0.188236915 -0.054552813 -0.0142410660 0.0064748053 -0.085510550 - actual$covM1$Item15[2, ] -0.054552810 0.020497166 0.0028843201 -0.0062397888 0.025359905 + expected$covM1$Item15[2, ] -0.054552813 0.020497167 0.0028843203 -0.0062397890 0.025359906 - actual$covM1$Item15[3, ] -0.014241065 0.002884320 0.0093444747 -0.0003460474 0.004556433 + expected$covM1$Item15[3, ] -0.014241066 0.002884320 0.0093444747 -0.0003460475 0.004556434 - actual$covM1$Item15[4, ] 0.006474805 -0.006239789 -0.0003460474 0.0087364918 -0.003001133 + expected$covM1$Item15[4, ] 0.006474805 -0.006239789 -0.0003460475 0.0087364918 -0.003001133 - actual$covM1$Item15[5, ] -0.085510546 0.025359905 0.0045564334 -0.0030011328 0.039767912 + expected$covM1$Item15[5, ] -0.085510550 0.025359906 0.0045564337 -0.0030011331 0.039767914 actual$covM1$Item16 vs expected$covM1$Item16 [,1] [,2] [,3] [,4] [,5] - actual$covM1$Item16[1, ] 0.044297510 -0.021564264 -0.0064130492 -0.001938253 -0.0102238774 + expected$covM1$Item16[1, ] 0.044297508 -0.021564263 -0.0064130417 -0.001938256 -0.0102238778 - actual$covM1$Item16[2, ] -0.021564264 0.018623907 0.0017297361 -0.006306357 0.0053161670 + expected$covM1$Item16[2, ] -0.021564263 0.018623907 0.0017297317 -0.006306356 0.0053161675 - actual$covM1$Item16[3, ] -0.006413049 0.001729736 0.0144304358 -0.005175775 -0.0002274551 + expected$covM1$Item16[3, ] -0.006413042 0.001729732 0.0144304360 -0.005175775 -0.0002274572 - actual$covM1$Item16[4, ] -0.001938253 -0.006306357 -0.0051757745 0.018704111 0.0011715047 + expected$covM1$Item16[4, ] -0.001938256 -0.006306356 -0.0051757752 0.018704111 0.0011715055 - actual$covM1$Item16[5, ] -0.010223877 0.005316167 -0.0002274551 0.001171505 0.0028091787 + expected$covM1$Item16[5, ] -0.010223878 0.005316167 -0.0002274572 0.001171505 0.0028091791 ── Failure ('test-difNLR.R:90:3'): difNLR - examples at help page ────────────── Expected `fit5` to equal `fit5_expected`. Differences: actual$nlrPAR[[8]] | expected$nlrPAR[[8]] [1] 0.126421690 - 0.126421840 [1] [2] 0.821916864 - 0.821916793 [2] [3] 0.000000000 | 0.000000000 [3] [4] 0.000000000 | 0.000000000 [4] [5] 0.188560897 - 0.188560839 [5] actual$nlrPAR[[18]] | expected$nlrPAR[[18]] [1] -0.534038569 - -0.534038548 [1] [2] 1.013003039 - 1.013003027 [2] [3] 0.000000000 | 0.000000000 [3] [4] 0.000000000 | 0.000000000 [4] [5] 0.029234301 - 0.029234295 [5] actual$nlrSE[[8]] | expected$nlrSE[[8]] [1] 0.343040215 - 0.343040109 [1] [2] 0.169509293 - 0.169509153 [2] [3] 0.000000000 | 0.000000000 [3] [4] 0.000000000 | 0.000000000 [4] [5] 0.131168793 - 0.131168755 [5] actual$nlrSE[[18]] | expected$nlrSE[[18]] [1] 0.211057550 - 0.211057621 [1] [2] 0.135836648 - 0.135836674 [2] [3] 0.000000000 | 0.000000000 [3] [4] 0.000000000 | 0.000000000 [4] [5] 0.061792800 - 0.061792815 [5] `actual$parM0$Item2`: -0.03058685 1.06428058 0.15099716 `expected$parM0$Item2`: -0.03058735 1.06428087 0.15099733 `actual$parM0$Item8`: 0.126421690 0.821916864 0.188560897 `expected$parM0$Item8`: 0.126421840 0.821916793 0.188560839 `actual$parM0$Item18`: -0.534038569 1.013003039 0.029234301 `expected$parM0$Item18`: -0.534038548 1.013003027 0.029234295 `actual$seM0$Item2`: 0.227735222 0.159226517 0.078461058 `expected$seM0$Item2`: 0.227735124 0.159226423 0.078460978 `actual$seM0$Item8`: 0.343040215 0.169509293 0.131168793 `expected$seM0$Item8`: 0.343040109 0.169509153 0.131168755 `actual$seM0$Item18`: 0.211057550 0.135836648 0.061792800 `expected$seM0$Item18`: 0.211057621 0.135836674 0.061792815 actual$covM0$Item2 vs expected$covM0$Item2 [,1] [,2] [,3] - actual$covM0$Item2[1, ] 0.05186333 -0.03021743 -0.017273062 + expected$covM0$Item2[1, ] 0.05186329 -0.03021739 -0.017273036 - actual$covM0$Item2[2, ] -0.03021743 0.02535308 0.010717985 + expected$covM0$Item2[2, ] -0.03021739 0.02535305 0.010717964 - actual$covM0$Item2[3, ] -0.01727306 0.01071799 0.006156138 + expected$covM0$Item2[3, ] -0.01727304 0.01071796 0.006156125 actual$covM0$Item8 vs expected$covM0$Item8 [,1] [,2] [,3] - actual$covM0$Item8[1, ] 0.11767659 -0.05143602 -0.04434402 + expected$covM0$Item8[1, ] 0.11767652 -0.05143595 -0.04434399 - actual$covM0$Item8[2, ] -0.05143602 0.02873340 0.02007202 + expected$covM0$Item8[2, ] -0.05143595 0.02873335 0.02007199 - actual$covM0$Item8[3, ] -0.04434402 0.02007202 0.01720525 + expected$covM0$Item8[3, ] -0.04434399 0.02007199 0.01720524 actual$covM0$Item12 vs expected$covM0$Item12 [,1] [,2] [,3] actual$covM0$Item12[1, ] 0.09424373 -0.04727163 -0.025621089 - actual$covM0$Item12[2, ] -0.04727163 0.03036815 0.013120782 + expected$covM0$Item12[2, ] -0.04727163 0.03036815 0.013120783 actual$covM0$Item12[3, ] -0.02562109 0.01312078 0.007285343 actual$covM0$Item18 vs expected$covM0$Item18 [,1] [,2] [,3] - actual$covM0$Item18[1, ] 0.04454529 -0.025026748 -0.012609287 + expected$covM0$Item18[1, ] 0.04454532 -0.025026763 -0.012609295 - actual$covM0$Item18[2, ] -0.02502675 0.018451595 0.007195376 + expected$covM0$Item18[2, ] -0.02502676 0.018451602 0.007195379 - actual$covM0$Item18[3, ] -0.01260929 0.007195376 0.003818350 + expected$covM0$Item18[3, ] -0.01260929 0.007195379 0.003818352 actual$parM1$Item16 | expected$parM1$Item16 [1] -0.508151611 - -0.508151559 [1] [2] 1.100651976 - 1.100651988 [2] [3] 0.029151096 - 0.029150994 [3] [4] -0.052590851 - -0.052590882 [4] [5] 0.073900244 - 0.073900243 [5] actual$seM1$Item4 | expected$seM1$Item4 [1] 0.897540624 - 0.897540525 [1] [2] 0.181507894 - 0.181507878 [2] [3] 0.137200404 - 0.137200380 [3] [4] 0.123750967 - 0.123750957 [4] [5] 0.683551339 - 0.683551268 [5] actual$seM1$Item15 | expected$seM1$Item15 [1] 0.479733456 - 0.479733216 [1] [2] 0.176905109 - 0.176905028 [2] [3] 0.096186780 - 0.096186799 [3] [4] 0.110288350 - 0.110288371 [4] [5] 0.226535938 - 0.226535796 [5] actual$seM1$Item16 | expected$seM1$Item16 [1] 0.220720963 - 0.220720949 [1] [2] 0.168408323 - 0.168408322 [2] [3] 0.114789154 - 0.114789215 [3] [4] 0.154145713 - 0.154145671 [4] [5] 0.059226682 - 0.059226681 [5] actual$covM1$Item4 vs expected$covM1$Item4 [,1] [,2] [,3] [,4] [,5] - actual$covM1$Item4[1, ] 0.80557917 -0.142048112 -0.048403540 -0.020625834 -0.61089172 + expected$covM1$Item4[1, ] 0.80557899 -0.142048080 -0.048403512 -0.020625812 -0.61089159 - actual$covM1$Item4[2, ] -0.14204811 0.032945116 0.004242145 -0.003338457 0.11118230 + expected$covM1$Item4[2, ] -0.14204808 0.032945110 0.004242140 -0.003338460 0.11118227 - actual$covM1$Item4[3, ] -0.04840354 0.004242145 0.018823951 0.009679530 0.03176359 + expected$covM1$Item4[3, ] -0.04840351 0.004242140 0.018823944 0.009679526 0.03176357 - actual$covM1$Item4[4, ] -0.02062583 -0.003338457 0.009679530 0.015314302 0.01323938 + expected$covM1$Item4[4, ] -0.02062581 -0.003338460 0.009679526 0.015314299 0.01323936 - actual$covM1$Item4[5, ] -0.61089172 0.111182295 0.031763587 0.013239378 0.46724243 + expected$covM1$Item4[5, ] -0.61089159 0.111182271 0.031763567 0.013239362 0.46724234 actual$covM1$Item15 vs expected$covM1$Item15 [,1] [,2] [,3] [,4] [,5] - actual$covM1$Item15[1, ] 0.23014419 -0.075673598 -0.0164225626 0.0142602206 -0.107645624 + expected$covM1$Item15[1, ] 0.23014396 -0.075673516 -0.0164225838 0.0142602480 -0.107645501 - actual$covM1$Item15[2, ] -0.07567360 0.031295417 0.0039230077 -0.0106777284 0.036138771 + expected$covM1$Item15[2, ] -0.07567352 0.031295389 0.0039230145 -0.0106777372 0.036138728 - actual$covM1$Item15[3, ] -0.01642256 0.003923008 0.0092518967 -0.0003337938 0.005756856 + expected$covM1$Item15[3, ] -0.01642258 0.003923014 0.0092519002 -0.0003337978 0.005756865 - actual$covM1$Item15[4, ] 0.01426022 -0.010677728 -0.0003337938 0.0121635202 -0.006860811 + expected$covM1$Item15[4, ] 0.01426025 -0.010677737 -0.0003337978 0.0121635248 -0.006860823 - actual$covM1$Item15[5, ] -0.10764562 0.036138771 0.0057568560 -0.0068608112 0.051318531 + expected$covM1$Item15[5, ] -0.10764550 0.036138728 0.0057568649 -0.0068608232 0.051318467 actual$covM1$Item16 vs expected$covM1$Item16 [,1] [,2] [,3] [,4] [,5] - actual$covM1$Item16[1, ] 0.048717744 -0.029002734 -0.0078734670 0.0048213840 -0.0121378318 + expected$covM1$Item16[1, ] 0.048717737 -0.029002731 -0.0078734692 0.0048213806 -0.0121378308 - actual$covM1$Item16[2, ] -0.029002734 0.028361363 0.0037418245 -0.0134059539 0.0075068703 + expected$covM1$Item16[2, ] -0.029002731 0.028361363 0.0037418255 -0.0134059510 0.0075068701 - actual$covM1$Item16[3, ] -0.007873467 0.003741824 0.0131765499 -0.0057301894 0.0003339497 + expected$covM1$Item16[3, ] -0.007873469 0.003741825 0.0131765640 -0.0057301861 0.0003339493 - actual$covM1$Item16[4, ] 0.004821384 -0.013405954 -0.0057301894 0.0237609010 -0.0005185267 + expected$covM1$Item16[4, ] 0.004821381 -0.013405951 -0.0057301861 0.0237608880 -0.0005185269 - actual$covM1$Item16[5, ] -0.012137832 0.007506870 0.0003339497 -0.0005185267 0.0035077998 + expected$covM1$Item16[5, ] -0.012137831 0.007506870 0.0003339493 -0.0005185269 0.0035077997 ── Failure ('test-difNLR.R:98:3'): difNLR - examples at help page ────────────── Expected `fit6` to equal `fit6_expected`. Differences: actual$nlrPAR[[2]] | expected$nlrPAR[[2]] [1] 0.177472710 - 0.177473254 [1] [2] 0.330349777 - 0.330349701 [2] [3] -0.450899418 - -0.450899245 [3] [4] 0.431260691 - 0.431260473 [4] [5] 0.150360097 - 0.150359863 [5] actual$nlrPAR[[13]] | expected$nlrPAR[[13]] [1] 0.517161361 - 0.517161486 [1] [2] 0.880409704 - 0.880409640 [2] [3] 0.000000000 | 0.000000000 [3] [4] 0.000000000 | 0.000000000 [4] [5] 0.340100107 - 0.340100060 [5] actual$nlrSE[[2]] | expected$nlrSE[[2]] [1] 0.370317544 - 0.370317782 [1] [2] 0.096151384 - 0.096151362 [2] [3] 0.175279549 - 0.175279502 [3] [4] 0.199426775 - 0.199426759 [4] [5] 0.163932678 - 0.163932877 [5] actual$nlrSE[[13]] | expected$nlrSE[[13]] [1] 0.380400043 - 0.380400034 [1] [2] 0.196782418 - 0.196782426 [2] [3] 0.000000000 | 0.000000000 [3] [4] 0.000000000 | 0.000000000 [4] [5] 0.142054522 - 0.142054552 [5] actual$nlrSE[[18]] | expected$nlrSE[[18]] [1] 0.211097181 - 0.211097192 [1] [2] 0.130432714 - 0.130432711 [2] [3] 0.000000000 | 0.000000000 [3] [4] 0.000000000 | 0.000000000 [4] [5] 0.068716094 - 0.068716096 [5] `actual$parM0$Item13`: 0.517161361 0.880409704 0.340100107 `expected$parM0$Item13`: 0.517161486 0.880409640 0.340100060 `actual$seM0$Item13`: 0.380400043 0.196782418 0.142054522 `expected$seM0$Item13`: 0.380400034 0.196782426 0.142054552 `actual$seM0$Item18`: 0.211097181 0.130432714 0.068716094 `expected$seM0$Item18`: 0.211097192 0.130432711 0.068716096 actual$covM0$Item13 vs expected$covM0$Item13 [,1] [,2] [,3] - actual$covM0$Item13[1, ] 0.14470419 -0.06411145 -0.05308457 + expected$covM0$Item13[1, ] 0.14470419 -0.06411146 -0.05308458 - actual$covM0$Item13[2, ] -0.06411145 0.03872332 0.02506125 + expected$covM0$Item13[2, ] -0.06411146 0.03872332 0.02506126 - actual$covM0$Item13[3, ] -0.05308457 0.02506125 0.02017949 + expected$covM0$Item13[3, ] -0.05308458 0.02506126 0.02017950 actual$covM0$Item18 vs expected$covM0$Item18 [,1] [,2] [,3] - actual$covM0$Item18[1, ] 0.04456202 -0.024053316 -0.014090872 + expected$covM0$Item18[1, ] 0.04456202 -0.024053317 -0.014090873 - actual$covM0$Item18[2, ] -0.02405332 0.017012693 0.007756721 + expected$covM0$Item18[2, ] -0.02405332 0.017012692 0.007756721 actual$covM0$Item18[3, ] -0.01409087 0.007756721 0.004721902 actual$parM1$Item2 | expected$parM1$Item2 [1] 0.177472710 - 0.177473254 [1] [2] 0.330349777 - 0.330349701 [2] [3] -0.450899418 - -0.450899245 [3] [4] 0.431260691 - 0.431260473 [4] [5] 0.150360097 - 0.150359863 [5] actual$parM1$Item14 | expected$parM1$Item14 [1] -0.424718732 - -0.424718652 [1] [2] 0.930618591 - 0.930618543 [2] [3] 0.045223607 - 0.045223635 [3] [4] -0.080158239 - -0.080158236 [4] [5] 0.048109543 - 0.048109514 [5] actual$parM1$Item19 | expected$parM1$Item19 [1] -0.138452239 - -0.138453420 [1] [2] 0.991225098 - 0.991225705 [2] [3] 0.064397463 - 0.064397656 [3] [4] -0.209346522 - -0.209346606 [4] [5] 0.008790044 - 0.008790492 [5] actual$parM1$Item20 | expected$parM1$Item20 [1] -0.976893375 - -0.976893389 [1] [2] 1.117935443 - 1.117935456 [2] [3] -0.419267085 - -0.419267083 [3] [4] 0.218094706 - 0.218094691 [4] [5] 0.175380839 - 0.175380840 [5] actual$seM1$Item2 | expected$seM1$Item2 [1] 0.370317544 - 0.370317782 [1] [2] 0.096151384 - 0.096151362 [2] [3] 0.175279549 - 0.175279502 [3] [4] 0.199426775 - 0.199426759 [4] [5] 0.163932678 - 0.163932877 [5] actual$seM1$Item14 | expected$seM1$Item14 [1] 0.260152167 - 0.260152196 [1] [2] 0.167126026 - 0.167126056 [2] [3] 0.106400891 - 0.106400876 [3] [4] 0.136296588 - 0.136296582 [4] [5] 0.080290594 - 0.080290613 [5] actual$seM1$Item19 | expected$seM1$Item19 [1] 0.248830906 - 0.248831216 [1] [2] 0.169914986 - 0.169915245 [2] [3] 0.098203305 - 0.098203377 [3] [4] 0.134438082 - 0.134438188 [4] [5] 0.094481052 - 0.094481002 [5] actual$seM1$Item20 | expected$seM1$Item20 [1] 0.232092240 - 0.232092242 [1] [2] 0.184527261 - 0.184527265 [2] [3] 0.198007123 - 0.198007134 [3] [4] 0.219756190 - 0.219756197 [4] [5] 0.038219442 | 0.038219442 [5] actual$covM1$Item2 vs expected$covM1$Item2 [,1] [,2] [,3] [,4] [,5] - actual$covM1$Item2[1, ] 0.13713508 -0.020291521 0.043707157 -0.056503115 -0.059469850 + expected$covM1$Item2[1, ] 0.13713526 -0.020291533 0.043707184 -0.056503164 -0.059469963 - actual$covM1$Item2[2, ] -0.02029152 0.009245089 -0.008364506 0.002923690 0.009423516 + expected$covM1$Item2[2, ] -0.02029153 0.009245084 -0.008364505 0.002923694 0.009423526 - actual$covM1$Item2[3, ] 0.04370716 -0.008364506 0.030722920 -0.021992221 -0.022251231 + expected$covM1$Item2[3, ] 0.04370718 -0.008364505 0.030722904 -0.021992215 -0.022251254 - actual$covM1$Item2[4, ] -0.05650312 0.002923690 -0.021992221 0.039771038 0.025279332 + expected$covM1$Item2[4, ] -0.05650316 0.002923694 -0.021992215 0.039771032 0.025279367 - actual$covM1$Item2[5, ] -0.05946985 0.009423516 -0.022251231 0.025279332 0.026873923 + expected$covM1$Item2[5, ] -0.05946996 0.009423526 -0.022251254 0.025279367 0.026873988 actual$covM1$Item14 vs expected$covM1$Item14 [,1] [,2] [,3] [,4] [,5] - actual$covM1$Item14[1, ] 0.067679150 -0.035427232 -0.0088074015 0.007413740 -0.0199846925 + expected$covM1$Item14[1, ] 0.067679165 -0.035427246 -0.0088073978 0.007413743 -0.0199846997 - actual$covM1$Item14[2, ] -0.035427232 0.027931109 0.0033840722 -0.012683715 0.0108774193 + expected$covM1$Item14[2, ] -0.035427246 0.027931119 0.0033840700 -0.012683717 0.0108774251 - actual$covM1$Item14[3, ] -0.008807401 0.003384072 0.0113211496 -0.003782322 0.0009940872 + expected$covM1$Item14[3, ] -0.008807398 0.003384070 0.0113211464 -0.003782317 0.0009940865 - actual$covM1$Item14[4, ] 0.007413740 -0.012683715 -0.0037823216 0.018576760 -0.0018409430 + expected$covM1$Item14[4, ] 0.007413743 -0.012683717 -0.0037823172 0.018576758 -0.0018409446 - actual$covM1$Item14[5, ] -0.019984693 0.010877419 0.0009940872 -0.001840943 0.0064465795 + expected$covM1$Item14[5, ] -0.019984700 0.010877425 0.0009940865 -0.001840945 0.0064465825 actual$covM1$Item19 vs expected$covM1$Item19 [,1] [,2] [,3] [,4] [,5] - actual$covM1$Item19[1, ] 0.061916820 -0.033995629 -0.009297763 0.011445863 -0.022559511 + expected$covM1$Item19[1, ] 0.061916974 -0.033995739 -0.009297808 0.011445886 -0.022559528 - actual$covM1$Item19[2, ] -0.033995629 0.028871102 0.003012574 -0.015593392 0.013283449 + expected$covM1$Item19[2, ] -0.033995739 0.028871191 0.003012605 -0.015593422 0.013283466 - actual$covM1$Item19[3, ] -0.009297763 0.003012574 0.009643889 -0.001762179 0.001738452 + expected$covM1$Item19[3, ] -0.009297808 0.003012605 0.009643903 -0.001762197 0.001738464 - actual$covM1$Item19[4, ] 0.011445863 -0.015593392 -0.001762179 0.018073598 -0.004360636 + expected$covM1$Item19[4, ] 0.011445886 -0.015593422 -0.001762197 0.018073626 -0.004360635 - actual$covM1$Item19[5, ] -0.022559511 0.013283449 0.001738452 -0.004360636 0.008926669 + expected$covM1$Item19[5, ] -0.022559528 0.013283466 0.001738464 -0.004360635 0.008926660 actual$covM1$Item20 vs expected$covM1$Item20 [,1] [,2] [,3] [,4] [,5] - actual$covM1$Item20[1, ] 0.0538668081 -0.0330202700 0.0001421080 -0.0001651629 -0.007745173 + expected$covM1$Item20[1, ] 0.0538668090 -0.0330202716 0.0001421085 -0.0001651634 -0.007745174 - actual$covM1$Item20[2, ] -0.0330202700 0.0340503099 0.0004877110 -0.0140608872 0.004665347 + expected$covM1$Item20[2, ] -0.0330202716 0.0340503117 0.0004877116 -0.0140608873 0.004665347 - actual$covM1$Item20[3, ] 0.0001421080 0.0004877110 0.0392068206 -0.0286260615 -0.002440810 + expected$covM1$Item20[3, ] 0.0001421085 0.0004877116 0.0392068249 -0.0286260662 -0.002440811 - actual$covM1$Item20[4, ] -0.0001651629 -0.0140608872 -0.0286260615 0.0482927830 0.001593366 + expected$covM1$Item20[4, ] -0.0001651634 -0.0140608873 -0.0286260662 0.0482927862 0.001593366 - actual$covM1$Item20[5, ] -0.0077451735 0.0046653466 -0.0024408104 0.0015933660 0.001460726 + expected$covM1$Item20[5, ] -0.0077451736 0.0046653468 -0.0024408107 0.0015933663 0.001460726 ── Failure ('test-difNLR.R:105:3'): difNLR - examples at help page ───────────── Expected `fit7` to equal `fit7_expected`. Differences: actual$nlrPAR[[6]] | expected$nlrPAR[[6]] [1] -2.860756754 - -2.860756501 [1] [2] 0.294079146 - 0.294079133 [2] [3] 0.000000000 | 0.000000000 [3] [4] 0.000000000 | 0.000000000 [4] [5] 0.061526225 - 0.061526174 [5] actual$nlrPAR[[7]] | expected$nlrPAR[[7]] [1] -2.129766181 - -2.129766291 [1] [2] 0.231768177 - 0.231768187 [2] [3] 0.491042687 - 0.491042882 [3] [4] -0.020552482 - -0.020552499 [4] [5] 0.000000000 | 0.000000000 [5] actual$nlrPAR[[14]] | expected$nlrPAR[[14]] [1] -3.761189285 - -3.761189457 [1] [2] 0.283317057 - 0.283317067 [2] [3] 0.000000000 | 0.000000000 [3] [4] 0.000000000 | 0.000000000 [4] [5] 0.071987400 - 0.071987417 [5] actual$nlrPAR[[18]] | expected$nlrPAR[[18]] [1] -4.318486592 - -4.318486900 [1] [2] 0.326161178 - 0.326161197 [2] [3] 0.000000000 | 0.000000000 [3] [4] 0.000000000 | 0.000000000 [4] [5] 0.029234264 - 0.029234292 [5] actual$nlrSE[[6]] | expected$nlrSE[[6]] [1] 0.800032077 - 0.800031842 [1] [2] 0.047732091 - 0.047732076 [2] [3] 0.000000000 | 0.000000000 [3] [4] 0.000000000 | 0.000000000 [4] [5] 0.147258537 - 0.147258520 [5] actual$nlrSE[[7]] | expected$nlrSE[[7]] [1] 1.112354923 - 1.112354869 [1] [2] 0.058267614 - 0.058267613 [2] [3] 0.456091925 | 0.456091925 [3] [4] 0.039381406 - 0.039381404 [4] [5] 0.279594382 - 0.279594362 [5] actual$nlrSE[[14]] | expected$nlrSE[[14]] [1] 0.793606682 - 0.793606758 [1] [2] 0.047284327 - 0.047284332 [2] [3] 0.000000000 | 0.000000000 [3] [4] 0.000000000 | 0.000000000 [4] [5] 0.079848859 - 0.079848857 [5] actual$nlrSE[[18]] | expected$nlrSE[[18]] [1] 0.699329966 - 0.699330191 [1] [2] 0.043735932 - 0.043735945 [2] [3] 0.000000000 | 0.000000000 [3] [4] 0.000000000 | 0.000000000 [4] [5] 0.061792805 - 0.061792814 [5] `actual$parM0$Item6`: -2.860756754 0.294079146 0.061526225 `expected$parM0$Item6`: -2.860756501 0.294079133 0.061526174 `actual$parM0$Item14`: -3.761189285 0.283317057 0.071987400 `expected$parM0$Item14`: -3.761189457 0.283317067 0.071987417 `actual$parM0$Item18`: -4.318486592 0.326161178 0.029234264 `expected$parM0$Item18`: -4.318486900 0.326161197 0.029234292 `actual$seM0$Item6`: 0.800032077 0.047732091 0.147258537 `expected$seM0$Item6`: 0.800031842 0.047732076 0.147258520 `actual$seM0$Item14`: 0.793606682 0.047284327 0.079848859 `expected$seM0$Item14`: 0.793606758 0.047284332 0.079848857 `actual$seM0$Item18`: 0.699329966 0.043735932 0.061792805 `expected$seM0$Item18`: 0.699330191 0.043735945 0.061792814 actual$covM0$Item6 vs expected$covM0$Item6 [,1] [,2] [,3] - actual$covM0$Item6[1, ] 0.64005132 -0.037549103 -0.112527497 + expected$covM0$Item6[1, ] 0.64005095 -0.037549080 -0.112527448 - actual$covM0$Item6[2, ] -0.03754910 0.002278352 0.006281254 + expected$covM0$Item6[2, ] -0.03754908 0.002278351 0.006281251 - actual$covM0$Item6[3, ] -0.11252750 0.006281254 0.021685077 + expected$covM0$Item6[3, ] -0.11252745 0.006281251 0.021685072 actual$covM0$Item14 vs expected$covM0$Item14 [,1] [,2] [,3] - actual$covM0$Item14[1, ] 0.62981157 -0.037094873 -0.059352624 + expected$covM0$Item14[1, ] 0.62981169 -0.037094881 -0.059352628 - actual$covM0$Item14[2, ] -0.03709487 0.002235808 0.003335018 + expected$covM0$Item14[2, ] -0.03709488 0.002235808 0.003335019 - actual$covM0$Item14[3, ] -0.05935262 0.003335018 0.006375840 + expected$covM0$Item14[3, ] -0.05935263 0.003335019 0.006375840 actual$covM0$Item18 vs expected$covM0$Item18 [,1] [,2] [,3] - actual$covM0$Item18[1, ] 0.48906240 -0.030252560 -0.039490274 + expected$covM0$Item18[1, ] 0.48906272 -0.030252579 -0.039490295 - actual$covM0$Item18[2, ] -0.03025256 0.001912832 0.002316727 + expected$covM0$Item18[2, ] -0.03025258 0.001912833 0.002316728 - actual$covM0$Item18[3, ] -0.03949027 0.002316727 0.003818351 + expected$covM0$Item18[3, ] -0.03949029 0.002316728 0.003818352 actual$parM1$Item6 | expected$parM1$Item6 [1] -2.765638706 - -2.765638638 [1] [2] 0.287085848 - 0.287085835 [2] [3] -0.161355944 - -0.161356361 [3] [4] 0.012414614 - 0.012414657 [4] [5] 0.058681823 - 0.058681846 [5] actual$parM1$Item7 | expected$parM1$Item7 [1] -2.129766181 - -2.129766291 [1] [2] 0.231768177 - 0.231768187 [2] [3] 0.491042687 - 0.491042882 [3] [4] -0.020552482 - -0.020552499 [4] [5] 0.000000000 | 0.000000000 [5] actual$parM1$Item9 | expected$parM1$Item9 [1] -2.290274718 - -2.290273162 [1] [2] 0.217155694 - 0.217155620 [2] [3] 0.277064738 - 0.277064638 [3] [4] -0.021873580 - -0.021873572 [4] [5] 0.053448573 - 0.053448232 [5] actual$parM1$Item13 | expected$parM1$Item13 [1] -1.695664217 - -1.695663657 [1] [2] 0.205923033 - 0.205923013 [2] [3] -0.760493891 - -0.760493588 [3] [4] 0.085496308 - 0.085496279 [4] [5] 0.218226211 - 0.218226052 [5] actual$parM1$Item18 | expected$parM1$Item18 [1] -4.597410312 - -4.597409267 [1] [2] 0.341940607 - 0.341940538 [2] [3] 0.426500848 - 0.426500064 [3] [4] -0.023314095 - -0.023314034 [4] [5] 0.033834987 - 0.033834929 [5] actual$seM1$Item6 | expected$seM1$Item6 [1] 0.812904466 - 0.812904715 [1] [2] 0.050646083 - 0.050646097 [2] [3] 0.473809295 - 0.473809386 [3] [4] 0.042832466 - 0.042832471 [4] [5] 0.148477253 - 0.148477291 [5] actual$seM1$Item7 | expected$seM1$Item7 [1] 1.112354923 - 1.112354869 [1] [2] 0.058267614 - 0.058267613 [2] [3] 0.456091925 | 0.456091925 [3] [4] 0.039381406 - 0.039381404 [4] [5] 0.279594382 - 0.279594362 [5] actual$seM1$Item9 | expected$seM1$Item9 [1] 1.106076330 - 1.106075897 [1] [2] 0.058189935 - 0.058189893 [2] [3] 0.438636888 - 0.438636645 [3] [4] 0.037649369 - 0.037649352 [4] [5] 0.225906353 - 0.225906565 [5] actual$seM1$Item13 | expected$seM1$Item13 [1] 1.022513375 - 1.022513431 [1] [2] 0.052074429 - 0.052074427 [2] [3] 0.545490371 - 0.545490169 [3] [4] 0.051390956 - 0.051390938 [4] [5] 0.235235576 - 0.235235702 [5] actual$seM1$Item18 | expected$seM1$Item18 [1] 0.806157612 - 0.806157708 [1] [2] 0.052593311 - 0.052593315 [2] [3] 0.586538776 - 0.586538612 [3] [4] 0.046215383 - 0.046215370 [4] [5] 0.059982953 - 0.059982978 [5] actual$covM1$Item6 vs expected$covM1$Item6 [,1] [,2] [,3] [,4] [,5] - actual$covM1$Item6[1, ] 0.660813670 -0.0400030143 -0.077742030 0.0071382098 -0.1103238235 + expected$covM1$Item6[1, ] 0.660814076 -0.0400030387 -0.077741933 0.0071382021 -0.1103238931 - actual$covM1$Item6[2, ] -0.040003014 0.0025650257 0.007898242 -0.0007554418 0.0060745901 + expected$covM1$Item6[2, ] -0.040003039 0.0025650272 0.007898237 -0.0007554414 0.0060745944 - actual$covM1$Item6[3, ] -0.077742030 0.0078982417 0.224495248 -0.0198123436 -0.0061886573 + expected$covM1$Item6[3, ] -0.077741933 0.0078982366 0.224495334 -0.0198123498 -0.0061886780 - actual$covM1$Item6[4, ] 0.007138210 -0.0007554418 -0.019812344 0.0018346201 0.0004926302 + expected$covM1$Item6[4, ] 0.007138202 -0.0007554414 -0.019812350 0.0018346206 0.0004926318 - actual$covM1$Item6[5, ] -0.110323824 0.0060745901 -0.006188657 0.0004926302 0.0220454945 + expected$covM1$Item6[5, ] -0.110323893 0.0060745944 -0.006188678 0.0004926318 0.0220455059 actual$covM1$Item7 vs expected$covM1$Item7 [,1] [,2] [,3] [,4] [,5] - actual$covM1$Item7[1, ] 1.23733347 -0.063366068 -0.31469687 0.022910429 -0.300610340 + expected$covM1$Item7[1, ] 1.23733335 -0.063366063 -0.31469690 0.022910432 -0.300610303 - actual$covM1$Item7[2, ] -0.06336607 0.003395115 0.01846587 -0.001426337 0.014629230 + expected$covM1$Item7[2, ] -0.06336606 0.003395115 0.01846587 -0.001426338 0.014629229 - actual$covM1$Item7[3, ] -0.31469687 0.018465870 0.20801984 -0.017413287 0.060680842 + expected$covM1$Item7[3, ] -0.31469690 0.018465872 0.20801984 -0.017413286 0.060680849 - actual$covM1$Item7[4, ] 0.02291043 -0.001426337 -0.01741329 0.001550895 -0.004108836 + expected$covM1$Item7[4, ] 0.02291043 -0.001426338 -0.01741329 0.001550895 -0.004108837 - actual$covM1$Item7[5, ] -0.30061034 0.014629230 0.06068084 -0.004108836 0.078173019 + expected$covM1$Item7[5, ] -0.30061030 0.014629229 0.06068085 -0.004108837 0.078173007 actual$covM1$Item9 vs expected$covM1$Item9 [,1] [,2] [,3] [,4] [,5] - actual$covM1$Item9[1, ] 1.22340485 -0.062963747 -0.19961493 0.016058281 -0.239970298 + expected$covM1$Item9[1, ] 1.22340389 -0.062963675 -0.19961479 0.016058269 -0.239970432 - actual$covM1$Item9[2, ] -0.06296375 0.003386069 0.01307774 -0.001100515 0.011692984 + expected$covM1$Item9[2, ] -0.06296367 0.003386064 0.01307773 -0.001100514 0.011692985 - actual$covM1$Item9[3, ] -0.19961493 0.013077745 0.19240232 -0.016085562 0.022244323 + expected$covM1$Item9[3, ] -0.19961479 0.013077732 0.19240211 -0.016085545 0.022244344 - actual$covM1$Item9[4, ] 0.01605828 -0.001100515 -0.01608556 0.001417475 -0.001717755 + expected$covM1$Item9[4, ] 0.01605827 -0.001100514 -0.01608555 0.001417474 -0.001717757 - actual$covM1$Item9[5, ] -0.23997030 0.011692984 0.02224432 -0.001717755 0.051033680 + expected$covM1$Item9[5, ] -0.23997043 0.011692985 0.02224434 -0.001717757 0.051033776 actual$covM1$Item13 vs expected$covM1$Item13 [,1] [,2] [,3] [,4] [,5] - actual$covM1$Item13[1, ] 1.04553360 -5.082766e-02 0.0963631853 -1.078600e-02 -0.227807363 + expected$covM1$Item13[1, ] 1.04553372 -5.082766e-02 0.0963631018 -1.078600e-02 -0.227807504 - actual$covM1$Item13[2, ] -0.05082766 2.711746e-03 0.0004969759 3.961036e-06 0.010040925 + expected$covM1$Item13[2, ] -0.05082766 2.711746e-03 0.0004969787 3.960824e-06 0.010040930 - actual$covM1$Item13[3, ] 0.09636319 4.969759e-04 0.2975597447 -2.734946e-02 -0.049566222 + expected$covM1$Item13[3, ] 0.09636310 4.969787e-04 0.2975595246 -2.734944e-02 -0.049566213 - actual$covM1$Item13[4, ] -0.01078600 3.961036e-06 -0.0273494553 2.641030e-03 0.004925403 + expected$covM1$Item13[4, ] -0.01078600 3.960824e-06 -0.0273494353 2.641029e-03 0.004925402 - actual$covM1$Item13[5, ] -0.22780736 1.004092e-02 -0.0495662225 4.925403e-03 0.055335776 + expected$covM1$Item13[5, ] -0.22780750 1.004093e-02 -0.0495662131 4.925402e-03 0.055335835 actual$covM1$Item18 vs expected$covM1$Item18 [,1] [,2] [,3] [,4] [,5] - actual$covM1$Item18[1, ] 0.64989010 -0.041811549 -0.245923882 0.0182463987 -0.0410512296 + expected$covM1$Item18[1, ] 0.64989025 -0.041811557 -0.245923777 0.0182463909 -0.0410512574 - actual$covM1$Item18[2, ] -0.04181155 0.002766056 0.017856116 -0.0013686293 0.0024343619 + expected$covM1$Item18[2, ] -0.04181156 0.002766057 0.017856109 -0.0013686287 0.0024343635 - actual$covM1$Item18[3, ] -0.24592388 0.017856116 0.344027736 -0.0266760285 0.0056454178 + expected$covM1$Item18[3, ] -0.24592378 0.017856109 0.344027543 -0.0266760134 0.0056454190 - actual$covM1$Item18[4, ] 0.01824640 -0.001368629 -0.026676028 0.0021358616 -0.0003689830 + expected$covM1$Item18[4, ] 0.01824639 -0.001368629 -0.026676013 0.0021358604 -0.0003689831 - actual$covM1$Item18[5, ] -0.04105123 0.002434362 0.005645418 -0.0003689830 0.0035979546 + expected$covM1$Item18[5, ] -0.04105126 0.002434364 0.005645419 -0.0003689831 0.0035979576 ── Failure ('test-difNLR.R:112:3'): difNLR - examples at help page ───────────── Expected `fit8` to equal `fit8_expected`. Differences: actual$nlrPAR[[2]] | expected$nlrPAR[[2]] [1] 0.597985900 - 0.597985847 [1] [2] 1.156918830 - 1.156918663 [2] [3] -0.514219172 - -0.514219097 [3] [4] 0.111767616 - 0.111767570 [4] [5] 0.920269609 - 0.920269643 [5] actual$nlrSE[[2]] | expected$nlrSE[[2]] [1] 0.256279444 - 0.256279449 [1] [2] 0.335866760 - 0.335866689 [2] [3] 0.193369361 - 0.193369325 [3] [4] 0.107757106 - 0.107757121 [4] [5] 0.073776161 - 0.073776176 [5] actual$nlrSE[[7]] | expected$nlrSE[[7]] [1] 0.498615859 - 0.498615829 [1] [2] 0.446131728 - 0.446131722 [2] [3] 0.201181080 | 0.201181080 [3] [4] 0.485314866 - 0.485314861 [4] [5] 0.166305546 | 0.166305546 [5] actual$nlrSE[[20]] | expected$nlrSE[[20]] [1] 0.284859518 - 0.284859523 [1] [2] 0.399762956 - 0.399762947 [2] [3] 0.000000000 | 0.000000000 [3] [4] 0.044980133 - 0.044980132 [4] [5] 0.187248913 - 0.187248908 [5] `actual$seM0$Item20`: 0.284859518 0.399762956 0.044980133 0.187248913 `expected$seM0$Item20`: 0.284859523 0.399762947 0.044980132 0.187248908 actual$covM0$Item20 vs expected$covM0$Item20 [,1] [,2] [,3] [,4] - actual$covM0$Item20[1, ] 0.081144945 -0.01830561 -0.006659124 -0.015218388 + expected$covM0$Item20[1, ] 0.081144948 -0.01830561 -0.006659124 -0.015218388 - actual$covM0$Item20[2, ] -0.018305606 0.15981042 0.015099960 -0.065639907 + expected$covM0$Item20[2, ] -0.018305608 0.15981041 0.015099959 -0.065639903 actual$covM0$Item20[3, ] -0.006659124 0.01509996 0.002023212 -0.005189033 - actual$covM0$Item20[4, ] -0.015218388 -0.06563991 -0.005189033 0.035062155 + expected$covM0$Item20[4, ] -0.015218388 -0.06563990 -0.005189033 0.035062154 actual$parM1$Item2 | expected$parM1$Item2 [1] 0.597985900 - 0.597985847 [1] [2] 1.156918830 - 1.156918663 [2] [3] -0.514219172 - -0.514219097 [3] [4] 0.111767616 - 0.111767570 [4] [5] 0.920269609 - 0.920269643 [5] actual$parM1$Item3 | expected$parM1$Item3 [1] 1.507978797 - 1.507978832 [1] [2] 0.917941861 - 0.917941949 [2] [3] 0.075486495 - 0.075486759 [3] [4] 0.000000000 | 0.000000000 [4] [5] 0.903360998 - 0.903360977 [5] actual$parM1$Item8 | expected$parM1$Item8 [1] 0.082385659 - 0.082385051 [1] [2] 0.830626126 - 0.830626403 [2] [3] 0.046172880 - 0.046172822 [3] [4] 0.196301810 - 0.196302045 [4] [5] 1.000000000 | 1.000000000 [5] `actual$parM1$Item9`: 0.24040932 0.63539679 0.03016191 0.04631914 1.00000000 `expected$parM1$Item9`: 0.24040832 0.63539707 0.03016213 0.04631958 1.00000000 actual$parM1$Item12 | expected$parM1$Item12 [1] -0.433876993 - -0.433876975 [1] [2] 0.921956021 - 0.921956009 [2] [3] 0.099762694 - 0.099762693 [3] [4] 0.220123115 - 0.220123110 [4] [5] 1.000000000 | 1.000000000 [5] actual$parM1$Item16 | expected$parM1$Item16 [1] -0.510654562 - -0.510654210 [1] [2] 1.080508002 - 1.080507944 [2] [3] 0.015599456 - 0.015598920 [3] [4] 0.076472713 - 0.076472689 [4] [5] 1.000000000 | 1.000000000 [5] actual$parM1$Item18 | expected$parM1$Item18 [1] -0.600332460 - -0.600332436 [1] [2] 1.014631448 - 1.014631427 [2] [3] 0.134705322 - 0.134705348 [3] [4] 0.028827923 - 0.028827912 [4] [5] 1.000000000 | 1.000000000 [5] `actual$parM1$Item19`: -0.18778238 0.85453878 0.14282620 0.01274498 1.00000000 `expected$parM1$Item19`: -0.18778293 0.85453903 0.14282632 0.01274518 1.00000000 actual$parM1$Item20 | expected$parM1$Item20 [1] -1.192646663 - -1.192646644 [1] [2] 1.218708172 - 1.218708167 [2] [3] -0.193708132 - -0.193708151 [3] [4] 0.195847969 - 0.195847968 [4] [5] 1.000000000 | 1.000000000 [5] actual$seM1$Item2 | expected$seM1$Item2 [1] 0.256279444 - 0.256279449 [1] [2] 0.335866760 - 0.335866689 [2] [3] 0.193369361 - 0.193369325 [3] [4] 0.107757106 - 0.107757121 [4] [5] 0.073776161 - 0.073776176 [5] actual$seM1$Item3 | expected$seM1$Item3 [1] 0.521636660 - 0.521636597 [1] [2] 0.468280887 - 0.468280877 [2] [3] 0.139852135 - 0.139852165 [3] [4] 0.548974196 - 0.548974120 [4] [5] 0.065226156 - 0.065226143 [5] actual$seM1$Item7 | expected$seM1$Item7 [1] 0.498615859 - 0.498615829 [1] [2] 0.446131728 - 0.446131722 [2] [3] 0.201181080 | 0.201181080 [3] [4] 0.485314866 - 0.485314861 [4] [5] 0.166305546 | 0.166305546 [5] actual$seM1$Item8 | expected$seM1$Item8 [1] 0.357743238 - 0.357742983 [1] [2] 0.449173685 - 0.449173523 [2] [3] 0.120023338 - 0.120023326 [3] [4] 0.199479185 - 0.199478897 [4] [5] 0.168186212 - 0.168186104 [5] actual$seM1$Item9 | expected$seM1$Item9 [1] 0.474256177 - 0.474255764 [1] [2] 0.533609220 - 0.533608761 [2] [3] 0.101035059 - 0.101035143 [3] [4] 0.444278193 - 0.444276968 [4] [5] 0.288364865 - 0.288364587 [5] actual$seM1$Item12 | expected$seM1$Item12 [1] 0.336646938 - 0.336646944 [1] [2] 0.435268570 - 0.435268587 [2] [3] 0.136765439 - 0.136765472 [3] [4] 0.118180623 - 0.118180630 [4] [5] 0.191265456 - 0.191265468 [5] actual$seM1$Item16 | expected$seM1$Item16 [1] 0.233415158 - 0.233415147 [1] [2] 0.317610829 - 0.317610849 [2] [3] 0.109179248 - 0.109179312 [3] [4] 0.079269037 - 0.079269049 [4] [5] 0.140601358 - 0.140601371 [5] actual$seM1$Item18 | expected$seM1$Item18 [1] 0.235640824 - 0.235640788 [1] [2] 0.311619233 - 0.311619184 [2] [3] 0.109317175 - 0.109317182 [3] [4] 0.086003787 - 0.086003770 [4] [5] 0.162425735 - 0.162425751 [5] actual$seM1$Item19 | expected$seM1$Item19 [1] 0.279334792 - 0.279334683 [1] [2] 0.353276217 - 0.353276835 [2] [3] 0.112688877 - 0.112688973 [3] [4] 0.159165465 - 0.159165509 [4] [5] 0.182483039 - 0.182483219 [5] actual$seM1$Item20 | expected$seM1$Item20 [1] 0.292370307 - 0.292370300 [1] [2] 0.394802887 - 0.394802900 [2] [3] 0.161955537 - 0.161955547 [3] [4] 0.047927639 - 0.047927641 [4] [5] 0.192097512 - 0.192097516 [5] actual$covM1$Item2 vs expected$covM1$Item2 [,1] [,2] [,3] [,4] [,5] - actual$covM1$Item2[1, ] 0.065679154 0.02566519 -0.019609044 -0.002997836 -0.012192613 + expected$covM1$Item2[1, ] 0.065679156 0.02566518 -0.019609039 -0.002997838 -0.012192615 - actual$covM1$Item2[2, ] 0.025665190 0.11280648 -0.048561392 0.031702945 -0.021355351 + expected$covM1$Item2[2, ] 0.025665183 0.11280643 -0.048561371 0.031702942 -0.021355351 - actual$covM1$Item2[3, ] -0.019609044 -0.04856139 0.037391710 -0.014072577 0.009551815 + expected$covM1$Item2[3, ] -0.019609039 -0.04856137 0.037391696 -0.014072576 0.009551815 - actual$covM1$Item2[4, ] -0.002997836 0.03170294 -0.014072577 0.011611594 -0.005036032 + expected$covM1$Item2[4, ] -0.002997838 0.03170294 -0.014072576 0.011611597 -0.005036033 - actual$covM1$Item2[5, ] -0.012192613 -0.02135535 0.009551815 -0.005036032 0.005442922 + expected$covM1$Item2[5, ] -0.012192615 -0.02135535 0.009551815 -0.005036033 0.005442924 actual$covM1$Item3 vs expected$covM1$Item3 [,1] [,2] [,3] [,4] [,5] - actual$covM1$Item3[1, ] 0.272104806 -0.12863746 -0.020122594 -0.22500310 0.006606135 + expected$covM1$Item3[1, ] 0.272104740 -0.12863744 -0.020122622 -0.22500304 0.006606133 - actual$covM1$Item3[2, ] -0.128637462 0.21928699 0.018169230 0.23931738 -0.027586335 + expected$covM1$Item3[2, ] -0.128637437 0.21928698 0.018169284 0.23931734 -0.027586329 - actual$covM1$Item3[3, ] -0.020122594 0.01816923 0.019558620 0.02033012 -0.002319488 + expected$covM1$Item3[3, ] -0.020122622 0.01816928 0.019558628 0.02033018 -0.002319495 - actual$covM1$Item3[4, ] -0.225003100 0.23931738 0.020330125 0.30137267 -0.026579272 + expected$covM1$Item3[4, ] -0.225003038 0.23931734 0.020330182 0.30137258 -0.026579263 - actual$covM1$Item3[5, ] 0.006606135 -0.02758634 -0.002319488 -0.02657927 0.004254451 + expected$covM1$Item3[5, ] 0.006606133 -0.02758633 -0.002319495 -0.02657926 0.004254450 actual$covM1$Item7 vs expected$covM1$Item7 [,1] [,2] [,3] [,4] [,5] - actual$covM1$Item7[1, ] 0.24861777 -0.13168198 -0.05711450 -0.19363009 0.02680847 + expected$covM1$Item7[1, ] 0.24861774 -0.13168198 -0.05711449 -0.19363008 0.02680848 - actual$covM1$Item7[2, ] -0.13168198 0.19903352 0.07840870 0.20584411 -0.06988717 + expected$covM1$Item7[2, ] -0.13168198 0.19903351 0.07840870 0.20584410 -0.06988717 - actual$covM1$Item7[3, ] -0.05711450 0.07840870 0.04047383 0.08202830 -0.02784201 + expected$covM1$Item7[3, ] -0.05711449 0.07840870 0.04047383 0.08202830 -0.02784201 - actual$covM1$Item7[4, ] -0.19363009 0.20584411 0.08202830 0.23553052 -0.06625726 + expected$covM1$Item7[4, ] -0.19363008 0.20584410 0.08202830 0.23553051 -0.06625726 - actual$covM1$Item7[5, ] 0.02680847 -0.06988717 -0.02784201 -0.06625726 0.02765753 + expected$covM1$Item7[5, ] 0.02680848 -0.06988717 -0.02784201 -0.06625726 0.02765753 actual$covM1$Item8 vs expected$covM1$Item8 [,1] [,2] [,3] [,4] [,5] - actual$covM1$Item8[1, ] 0.127980224 -0.01853840 -0.007895556 -0.030616899 -0.013483788 + expected$covM1$Item8[1, ] 0.127980042 -0.01853835 -0.007895529 -0.030616822 -0.013483787 - actual$covM1$Item8[2, ] -0.018538398 0.20175700 0.011072014 0.083461529 -0.069883402 + expected$covM1$Item8[2, ] -0.018538353 0.20175685 0.011071960 0.083461376 -0.069883326 - actual$covM1$Item8[3, ] -0.007895556 0.01107201 0.014405602 0.004651151 -0.003904769 + expected$covM1$Item8[3, ] -0.007895529 0.01107196 0.014405599 0.004651120 -0.003904753 - actual$covM1$Item8[4, ] -0.030616899 0.08346153 0.004651151 0.039791945 -0.025890385 + expected$covM1$Item8[4, ] -0.030616822 0.08346138 0.004651120 0.039791830 -0.025890328 - actual$covM1$Item8[5, ] -0.013483788 -0.06988340 -0.003904769 -0.025890385 0.028286602 + expected$covM1$Item8[5, ] -0.013483787 -0.06988333 -0.003904753 -0.025890328 0.028286566 actual$covM1$Item9 vs expected$covM1$Item9 [,1] [,2] [,3] [,4] [,5] - actual$covM1$Item9[1, ] 0.224918921 -0.07447685 -0.008330165 -0.11316741 0.002628247 + expected$covM1$Item9[1, ] 0.224918530 -0.07447642 -0.008330201 -0.11316683 0.002628051 - actual$covM1$Item9[2, ] -0.074476847 0.28473880 0.013489919 0.22727230 -0.146796537 + expected$covM1$Item9[2, ] -0.074476424 0.28473831 0.013490030 0.22727146 -0.146796259 - actual$covM1$Item9[3, ] -0.008330165 0.01348992 0.010208083 0.01085350 -0.007013488 + expected$covM1$Item9[3, ] -0.008330201 0.01349003 0.010208100 0.01085357 -0.007013541 - actual$covM1$Item9[4, ] -0.113167413 0.22727230 0.010853503 0.19738311 -0.108918602 + expected$covM1$Item9[4, ] -0.113166827 0.22727146 0.010853575 0.19738202 -0.108918167 - actual$covM1$Item9[5, ] 0.002628247 -0.14679654 -0.007013488 -0.10891860 0.083154295 + expected$covM1$Item9[5, ] 0.002628051 -0.14679626 -0.007013541 -0.10891817 0.083154135 actual$covM1$Item12 vs expected$covM1$Item12 [,1] [,2] [,3] [,4] [,5] - actual$covM1$Item12[1, ] 0.113331161 -0.002995872 -0.008519888 -0.014343800 -0.021811108 + expected$covM1$Item12[1, ] 0.113331165 -0.002995871 -0.008519885 -0.014343801 -0.021811109 - actual$covM1$Item12[2, ] -0.002995872 0.189458728 0.020012934 0.046964457 -0.076096847 + expected$covM1$Item12[2, ] -0.002995871 0.189458743 0.020012948 0.046964462 -0.076096856 - actual$covM1$Item12[3, ] -0.008519888 0.020012934 0.018704785 0.005060958 -0.008230797 + expected$covM1$Item12[3, ] -0.008519885 0.020012948 0.018704794 0.005060961 -0.008230804 - actual$covM1$Item12[4, ] -0.014343800 0.046964457 0.005060958 0.013966660 -0.016618142 + expected$covM1$Item12[4, ] -0.014343801 0.046964462 0.005060961 0.013966661 -0.016618144 - actual$covM1$Item12[5, ] -0.021811108 -0.076096847 -0.008230797 -0.016618142 0.036582475 + expected$covM1$Item12[5, ] -0.021811109 -0.076096856 -0.008230804 -0.016618144 0.036582479 actual$covM1$Item16 vs expected$covM1$Item16 [,1] [,2] [,3] [,4] [,5] - actual$covM1$Item16[1, ] 0.054482636 -0.002616064 -0.0059813192 -0.0073820474 -0.0118201506 + expected$covM1$Item16[1, ] 0.054482631 -0.002616035 -0.0059813191 -0.0073820407 -0.0118201626 - actual$covM1$Item16[2, ] -0.002616064 0.100876639 0.0014047195 0.0222772297 -0.0397191121 + expected$covM1$Item16[2, ] -0.002616035 0.100876651 0.0014046713 0.0222772350 -0.0397191188 - actual$covM1$Item16[3, ] -0.005981319 0.001404719 0.0119201083 0.0003188272 -0.0005713070 + expected$covM1$Item16[3, ] -0.005981319 0.001404671 0.0119201223 0.0003188151 -0.0005712869 - actual$covM1$Item16[4, ] -0.007382047 0.022277230 0.0003188272 0.0062835803 -0.0075099387 + expected$covM1$Item16[4, ] -0.007382041 0.022277235 0.0003188151 0.0062835822 -0.0075099410 - actual$covM1$Item16[5, ] -0.011820151 -0.039719112 -0.0005713070 -0.0075099387 0.0197687419 + expected$covM1$Item16[5, ] -0.011820163 -0.039719119 -0.0005712869 -0.0075099410 0.0197687456 actual$covM1$Item18 vs expected$covM1$Item18 [,1] [,2] [,3] [,4] [,5] - actual$covM1$Item18[1, ] 0.055526598 -0.005107745 -0.005777831 -0.008518791 -0.012020502 + expected$covM1$Item18[1, ] 0.055526581 -0.005107706 -0.005777826 -0.008518781 -0.012020516 - actual$covM1$Item18[2, ] -0.005107745 0.097106546 0.012484482 0.024001296 -0.045575976 + expected$covM1$Item18[2, ] -0.005107706 0.097106516 0.012484487 0.024001287 -0.045575976 - actual$covM1$Item18[3, ] -0.005777831 0.012484482 0.011950245 0.003163198 -0.006030887 + expected$covM1$Item18[3, ] -0.005777826 0.012484487 0.011950246 0.003163199 -0.006030892 - actual$covM1$Item18[4, ] -0.008518791 0.024001296 0.003163198 0.007396651 -0.009759212 + expected$covM1$Item18[4, ] -0.008518781 0.024001287 0.003163199 0.007396648 -0.009759212 - actual$covM1$Item18[5, ] -0.012020502 -0.045575976 -0.006030887 -0.009759212 0.026382119 + expected$covM1$Item18[5, ] -0.012020516 -0.045575976 -0.006030892 -0.009759212 0.026382125 actual$covM1$Item19 vs expected$covM1$Item19 [,1] [,2] [,3] [,4] [,5] - actual$covM1$Item19[1, ] 0.078027926 -0.01036264 -0.006297070 -0.018930362 -0.012313163 + expected$covM1$Item19[1, ] 0.078027865 -0.01036266 -0.006297082 -0.018930344 -0.012313140 - actual$covM1$Item19[2, ] -0.010362636 0.12480409 0.020498902 0.052043046 -0.059447447 + expected$covM1$Item19[2, ] -0.010362665 0.12480452 0.020498987 0.052043164 -0.059447623 - actual$covM1$Item19[3, ] -0.006297070 0.02049890 0.012698783 0.008692298 -0.009960841 + expected$covM1$Item19[3, ] -0.006297082 0.02049899 0.012698805 0.008692324 -0.009960876 - actual$covM1$Item19[4, ] -0.018930362 0.05204305 0.008692298 0.025333645 -0.022091699 + expected$covM1$Item19[4, ] -0.018930344 0.05204316 0.008692324 0.025333659 -0.022091746 - actual$covM1$Item19[5, ] -0.012313163 -0.05944745 -0.009960841 -0.022091699 0.033300060 + expected$covM1$Item19[5, ] -0.012313140 -0.05944762 -0.009960876 -0.022091746 0.033300125 actual$covM1$Item20 vs expected$covM1$Item20 [,1] [,2] [,3] [,4] [,5] - actual$covM1$Item20[1, ] 0.085480397 -0.001719418 -0.010946770 -0.005364424 -0.022613298 + expected$covM1$Item20[1, ] 0.085480392 -0.001719420 -0.010946766 -0.005364424 -0.022613297 - actual$covM1$Item20[2, ] -0.001719418 0.155869320 -0.023641346 0.016042893 -0.066798603 + expected$covM1$Item20[2, ] -0.001719420 0.155869330 -0.023641348 0.016042894 -0.066798608 - actual$covM1$Item20[3, ] -0.010946770 -0.023641346 0.026229596 -0.002529073 0.010504071 + expected$covM1$Item20[3, ] -0.010946766 -0.023641348 0.026229599 -0.002529074 0.010504071 - actual$covM1$Item20[4, ] -0.005364424 0.016042893 -0.002529073 0.002297059 -0.005781457 + expected$covM1$Item20[4, ] -0.005364424 0.016042894 -0.002529074 0.002297059 -0.005781457 - actual$covM1$Item20[5, ] -0.022613298 -0.066798603 0.010504071 -0.005781457 0.036901454 + expected$covM1$Item20[5, ] -0.022613297 -0.066798608 0.010504071 -0.005781457 0.036901456 ── Failure ('test-difNLR.R:118:3'): difNLR - examples at help page ───────────── Expected `fit9` to equal `fit9_expected`. Differences: `actual$nlrPAR[[7]]`: 0.552188439 0.689291692 0.272423602 0.000000000 `expected$nlrPAR[[7]]`: 0.552188451 0.689291690 0.272423576 0.000000000 `actual$nlrPAR[[10]]`: 0.281290398 0.611640553 0.092852151 0.000000000 `expected$nlrPAR[[10]]`: 0.281290404 0.611640549 0.092852128 0.000000000 `actual$nlrSE[[7]]`: 0.067721747 0.056696510 0.096835714 0.000000000 `expected$nlrSE[[7]]`: 0.067721748 0.056696509 0.096835717 0.000000000 `actual$nlrSE[[10]]`: 0.066083583 0.053634794 0.093527262 0.000000000 `expected$nlrSE[[10]]`: 0.066083583 0.053634793 0.093527266 0.000000000 `actual$parM0$Item7`: 0.552188439 0.689291692 0.272423602 `expected$parM0$Item7`: 0.552188451 0.689291690 0.272423576 `actual$parM0$Item10`: 0.281290398 0.611640553 0.092852151 `expected$parM0$Item10`: 0.281290404 0.611640549 0.092852128 `actual$seM0$Item7`: 0.067721747 0.056696510 0.096835714 `expected$seM0$Item7`: 0.067721748 0.056696509 0.096835717 `actual$seM0$Item10`: 0.066083583 0.053634794 0.093527262 `expected$seM0$Item10`: 0.066083583 0.053634793 0.093527266 actual$covM0$Item7 vs expected$covM0$Item7 [,1] [,2] [,3] - actual$covM0$Item7[1, ] 0.0045862351 0.0007942599 -0.0042944437 + expected$covM0$Item7[1, ] 0.0045862352 0.0007942599 -0.0042944438 - actual$covM0$Item7[2, ] 0.0007942599 0.0032144942 0.0003866655 + expected$covM0$Item7[2, ] 0.0007942599 0.0032144942 0.0003866656 - actual$covM0$Item7[3, ] -0.0042944437 0.0003866655 0.0093771555 + expected$covM0$Item7[3, ] -0.0042944438 0.0003866656 0.0093771560 actual$covM0$Item10 vs expected$covM0$Item10 [,1] [,2] [,3] - actual$covM0$Item10[1, ] 0.0043670400 0.0003455708 -0.0043116925 + expected$covM0$Item10[1, ] 0.0043670400 0.0003455710 -0.0043116927 - actual$covM0$Item10[2, ] 0.0003455708 0.0028766911 0.0001151672 + expected$covM0$Item10[2, ] 0.0003455710 0.0028766910 0.0001151668 - actual$covM0$Item10[3, ] -0.0043116925 0.0001151672 0.0087473487 + expected$covM0$Item10[3, ] -0.0043116927 0.0001151668 0.0087473494 `actual$parM1$Item1`: 0.555393692 0.995464991 -0.865126851 -0.152420868 `expected$parM1$Item1`: 0.555393699 0.995465005 -0.865126856 -0.152420890 `actual$parM1$Item7`: 0.559439988 0.719833883 0.252572198 -0.063833226 `expected$parM1$Item7`: 0.559439989 0.719833875 0.252572202 -0.063833206 `actual$seM1$Item1`: 0.074604012 0.094516054 0.100535023 0.126653526 `expected$seM1$Item1`: 0.074604014 0.094516056 0.100535024 0.126653528 `actual$seM1$Item7`: 0.069709755 0.079613910 0.102734359 0.113448595 `expected$seM1$Item7`: 0.069709754 0.079613908 0.102734368 0.113448579 actual$covM1$Item1 vs expected$covM1$Item1 [,1] [,2] [,3] [,4] actual$covM1$Item1[1, ] 0.005565759 0.002338469 -0.005565759 -0.002338469 - actual$covM1$Item1[2, ] 0.002338469 0.008933284 -0.002338469 -0.008933284 + expected$covM1$Item1[2, ] 0.002338469 0.008933285 -0.002338469 -0.008933285 - actual$covM1$Item1[3, ] -0.005565759 -0.002338469 0.010107291 0.001271138 + expected$covM1$Item1[3, ] -0.005565759 -0.002338469 0.010107291 0.001271137 - actual$covM1$Item1[4, ] -0.002338469 -0.008933284 0.001271138 0.016041116 + expected$covM1$Item1[4, ] -0.002338469 -0.008933285 0.001271137 0.016041116 actual$covM1$Item7 vs expected$covM1$Item7 [,1] [,2] [,3] [,4] actual$covM1$Item7[1, ] 0.004859450 0.001607575 -0.004859450 -0.001607575 - actual$covM1$Item7[2, ] 0.001607575 0.006338375 -0.001607574 -0.006338375 + expected$covM1$Item7[2, ] 0.001607575 0.006338374 -0.001607575 -0.006338374 - actual$covM1$Item7[3, ] -0.004859450 -0.001607574 0.010554349 0.003928016 + expected$covM1$Item7[3, ] -0.004859450 -0.001607575 0.010554350 0.003928018 - actual$covM1$Item7[4, ] -0.001607575 -0.006338375 0.003928016 0.012870584 + expected$covM1$Item7[4, ] -0.001607575 -0.006338374 0.003928018 0.012870580 ── Failure ('test-difNLR.R:125:3'): difNLR - examples at help page ───────────── Expected `fit10` to equal `fit10_expected`. Differences: actual$nlrPAR[[1]] | expected$nlrPAR[[1]] [1] 0.352215454 - 0.352215398 [1] [2] 1.162842181 - 1.162842153 [2] [3] -1.154791212 - -1.154791138 [3] [4] 0.134319300 - 0.134319297 [4] [5] 0.978822642 - 0.978822657 [5] [6] 0.021177358 - 0.021177343 [6] actual$nlrPAR[[9]] | expected$nlrPAR[[9]] [1] 0.247626831 - 0.247627610 [1] [2] 0.639745163 - 0.639744929 [2] [3] 0.000000000 | 0.000000000 [3] [4] 0.051677451 - 0.051677078 [4] [5] 0.997704129 - 0.997704122 [5] [6] 0.002295871 - 0.002295878 [6] actual$nlrPAR[[19]] | expected$nlrPAR[[19]] [1] -0.338788974 - -0.338789188 [1] [2] 1.026951113 - 1.026951238 [2] [3] 0.585537899 - 0.585537978 [3] [4] 0.061911742 - 0.061911810 [4] [5] 1.000000000 | 1.000000000 [5] [6] -0.149407491 - -0.149407505 [6] actual$nlrSE[[1]] | expected$nlrSE[[1]] [1] 0.244158662 - 0.244158657 [1] [2] 0.291674011 - 0.291674027 [2] [3] 0.341276352 - 0.341276339 [3] [4] 0.067339932 - 0.067339936 [4] [5] 0.083503178 - 0.083503183 [5] [6] 0.105691296 | 0.105691296 [6] actual$nlrSE[[7]] | expected$nlrSE[[7]] [1] 0.460828393 - 0.460828449 [1] [2] 0.445556540 - 0.445556550 [2] [3] 0.000000000 | 0.000000000 [3] [4] 0.477252118 - 0.477252149 [4] [5] 0.143832805 - 0.143832803 [5] [6] 0.030632741 | 0.030632741 [6] actual$nlrSE[[9]] | expected$nlrSE[[9]] [1] 0.464768649 - 0.464768825 [1] [2] 0.532542182 - 0.532542491 [2] [3] 0.000000000 | 0.000000000 [3] [4] 0.433897776 - 0.433898621 [4] [5] 0.286004872 - 0.286005089 [5] [6] 0.036888569 - 0.036888559 [6] actual$nlrSE[[19]] | expected$nlrSE[[19]] [1] 0.293265666 - 0.293265676 [1] [2] 0.345509359 - 0.345509334 [2] [3] 0.277210636 - 0.277210633 [3] [4] 0.110267583 - 0.110267544 [4] [5] 0.156016742 - 0.156016710 [5] [6] 0.104063828 - 0.104063823 [6] actual$parM0$Item9 | expected$parM0$Item9 [1] 0.247626831 - 0.247627610 [1] [2] 0.639745163 - 0.639744929 [2] [3] 0.051677451 - 0.051677078 [3] [4] 0.997704129 - 0.997704122 [4] [5] 0.002295871 - 0.002295878 [5] actual$seM0$Item7 | expected$seM0$Item7 [1] 0.460828393 - 0.460828449 [1] [2] 0.445556540 - 0.445556550 [2] [3] 0.477252118 - 0.477252149 [3] [4] 0.143832805 - 0.143832803 [4] [5] 0.030632741 | 0.030632741 [5] actual$seM0$Item9 | expected$seM0$Item9 [1] 0.464768649 - 0.464768825 [1] [2] 0.532542182 - 0.532542491 [2] [3] 0.433897776 - 0.433898621 [3] [4] 0.286004872 - 0.286005089 [4] [5] 0.036888569 - 0.036888559 [5] actual$covM0$Item7 vs expected$covM0$Item7 [,1] [,2] [,3] [,4] [,5] - actual$covM0$Item7[1, ] 0.212362808 -0.094143900 -0.156584884 0.0119019143 -0.0037323225 + expected$covM0$Item7[1, ] 0.212362860 -0.094143928 -0.156584925 0.0119019169 -0.0037323230 - actual$covM0$Item7[2, ] -0.094143900 0.198520630 0.200777938 -0.0601200805 0.0016901313 + expected$covM0$Item7[2, ] -0.094143928 0.198520639 0.200777955 -0.0601200800 0.0016901308 - actual$covM0$Item7[3, ] -0.156584884 0.200777938 0.227769584 -0.0556754197 0.0027817835 + expected$covM0$Item7[3, ] -0.156584925 0.200777955 0.227769613 -0.0556754204 0.0027817831 - actual$covM0$Item7[4, ] 0.011901914 -0.060120080 -0.055675420 0.0206878757 -0.0006579880 + expected$covM0$Item7[4, ] 0.011901917 -0.060120080 -0.055675420 0.0206878753 -0.0006579877 - actual$covM0$Item7[5, ] -0.003732323 0.001690131 0.002781783 -0.0006579880 0.0009383648 + expected$covM0$Item7[5, ] -0.003732323 0.001690131 0.002781783 -0.0006579877 0.0009383648 actual$covM0$Item9 vs expected$covM0$Item9 [,1] [,2] [,3] [,4] [,5] - actual$covM0$Item9[1, ] 0.2160098974 -6.476477e-02 -1.030129e-01 -0.0022779940 -1.760865e-04 + expected$covM0$Item9[1, ] 0.2160100606 -6.476500e-02 -1.030133e-01 -0.0022778702 -1.760864e-04 - actual$covM0$Item9[2, ] -0.0647647690 2.836012e-01 2.213988e-01 -0.1449467105 1.480259e-05 + expected$covM0$Item9[2, ] -0.0647650005 2.836015e-01 2.213994e-01 -0.1449469175 1.480101e-05 - actual$covM0$Item9[3, ] -0.1030129342 2.213988e-01 1.882673e-01 -0.1051302805 5.657609e-05 + expected$covM0$Item9[3, ] -0.1030132619 2.213994e-01 1.882680e-01 -0.1051305951 5.657496e-05 - actual$covM0$Item9[4, ] -0.0022779940 -1.449467e-01 -1.051303e-01 0.0817987867 -6.575088e-04 + expected$covM0$Item9[4, ] -0.0022778702 -1.449469e-01 -1.051306e-01 0.0817989109 -6.575075e-04 - actual$covM0$Item9[5, ] -0.0001760865 1.480259e-05 5.657609e-05 -0.0006575088 1.360767e-03 + expected$covM0$Item9[5, ] -0.0001760864 1.480101e-05 5.657496e-05 -0.0006575075 1.360766e-03 actual$parM1$Item1 | expected$parM1$Item1 [1] 0.352215454 - 0.352215398 [1] [2] 1.162842181 - 1.162842153 [2] [3] -1.154791212 - -1.154791138 [3] [4] 0.134319300 - 0.134319297 [4] [5] 0.978822642 - 0.978822657 [5] [6] 0.021177358 - 0.021177343 [6] actual$parM1$Item7 | expected$parM1$Item7 [1] 0.557582568 - 0.557582567 [1] [2] 0.712124977 - 0.712124987 [2] [3] 0.376374508 - 0.376374549 [3] [4] 0.000000000 | 0.000000000 [4] [5] 1.000000000 | 1.000000000 [5] [6] -0.030721342 - -0.030721351 [6] actual$parM1$Item8 | expected$parM1$Item8 [1] 0.373665964 - 0.373666098 [1] [2] 0.979433086 - 0.979433010 [2] [3] -0.351044400 - -0.351044421 [3] [4] 0.234844420 - 0.234844380 [4] [5] 0.886701304 - 0.886701302 [5] [6] 0.113298696 - 0.113298698 [6] actual$parM1$Item11 | expected$parM1$Item11 [1] 0.821799628 - 0.821799927 [1] [2] 0.892827642 - 0.892827427 [2] [3] -0.017299339 - -0.017299667 [3] [4] 0.035553945 - 0.035553763 [4] [5] 1.000000000 | 1.000000000 [5] [6] -0.014779686 - -0.014779605 [6] actual$parM1$Item14 | expected$parM1$Item14 [1] -0.501624851 - -0.501625019 [1] [2] 0.864989151 - 0.864989228 [2] [3] 0.111855727 - 0.111855771 [3] [4] 0.063395655 - 0.063395703 [4] [5] 1.000000000 | 1.000000000 [5] [6] 0.000000000 | 0.000000000 [6] actual$parM1$Item16 | expected$parM1$Item16 [1] -0.510654562 - -0.510654210 [1] [2] 1.080508002 - 1.080507944 [2] [3] 0.015599456 - 0.015598920 [3] [4] 0.076472713 - 0.076472689 [4] [5] 1.000000000 | 1.000000000 [5] [6] 0.000000000 | 0.000000000 [6] actual$parM1$Item19 | expected$parM1$Item19 [1] -0.338788974 - -0.338789188 [1] [2] 1.026951113 - 1.026951238 [2] [3] 0.585537899 - 0.585537978 [3] [4] 0.061911742 - 0.061911810 [4] [5] 1.000000000 | 1.000000000 [5] [6] -0.149407491 - -0.149407505 [6] actual$seM1$Item1 | expected$seM1$Item1 [1] 0.244158662 - 0.244158657 [1] [2] 0.291674011 - 0.291674027 [2] [3] 0.341276352 - 0.341276339 [3] [4] 0.067339932 - 0.067339936 [4] [5] 0.083503178 - 0.083503183 [5] [6] 0.105691296 | 0.105691296 [6] actual$seM1$Item7 | expected$seM1$Item7 [1] 0.515971332 - 0.515971341 [1] [2] 0.434907110 | 0.434907110 [2] [3] 0.303436166 - 0.303436189 [3] [4] 0.456730046 - 0.456730039 [4] [5] 0.168363776 - 0.168363774 [5] [6] 0.079816386 | 0.079816386 [6] actual$seM1$Item8 | expected$seM1$Item8 [1] 0.369078034 - 0.369078012 [1] [2] 0.441531312 - 0.441531342 [2] [3] 0.314818618 - 0.314818594 [3] [4] 0.145956190 - 0.145956228 [4] [5] 0.102592249 - 0.102592261 [5] [6] 0.089117030 - 0.089117032 [6] actual$seM1$Item11 | expected$seM1$Item11 [1] 0.321227742 - 0.321227844 [1] [2] 0.366683936 - 0.366684119 [2] [3] 0.216059242 - 0.216059189 [3] [4] 0.285346569 - 0.285346881 [4] [5] 0.098277328 - 0.098277317 [5] [6] 0.059399889 - 0.059399854 [6] actual$seM1$Item14 | expected$seM1$Item14 [1] 0.344221248 - 0.344221226 [1] [2] 0.379547996 - 0.379547886 [2] [3] 0.307898170 - 0.307898202 [3] [4] 0.127286626 - 0.127286559 [4] [5] 0.244256576 - 0.244256518 [5] [6] 0.137244654 - 0.137244661 [6] actual$seM1$Item16 | expected$seM1$Item16 [1] 0.263698611 - 0.263698513 [1] [2] 0.317694287 - 0.317694305 [2] [3] 0.265001070 - 0.265001108 [3] [4] 0.079289062 - 0.079289074 [4] [5] 0.151292397 - 0.151292374 [5] [6] 0.108903078 - 0.108903021 [6] actual$seM1$Item19 | expected$seM1$Item19 [1] 0.293265666 - 0.293265676 [1] [2] 0.345509359 - 0.345509334 [2] [3] 0.277210636 - 0.277210633 [3] [4] 0.110267583 - 0.110267544 [4] [5] 0.156016742 - 0.156016710 [5] [6] 0.104063828 - 0.104063823 [6] actual$seM1$Item20 | expected$seM1$Item20 [1] 0.328003094 - 0.328003101 [1] [2] 0.395824164 - 0.395824153 [2] [3] 0.391843491 - 0.391843523 [3] [4] 0.047961151 | 0.047961151 [4] [5] 0.200224027 - 0.200224040 [5] [6] 0.162426278 - 0.162426290 [6] actual$covM1$Item1 vs expected$covM1$Item1 [,1] [,2] [,3] [,4] [,5] [,6] - actual$covM1$Item1[1, ] 0.059613452 0.033895448 -0.03788850 0.0013905549 -0.0162716515 -0.0012268023 + expected$covM1$Item1[1, ] 0.059613450 0.033895451 -0.03788849 0.0013905550 -0.0162716520 -0.0012268058 - actual$covM1$Item1[2, ] 0.033895448 0.085073729 -0.05659170 0.0163633049 -0.0200493409 -0.0092486948 + expected$covM1$Item1[2, ] 0.033895451 0.085073738 -0.05659170 0.0163633066 -0.0200493442 -0.0092486969 - actual$covM1$Item1[3, ] -0.037888499 -0.056591699 0.11646955 -0.0140781627 0.0185985498 -0.0191014662 + expected$covM1$Item1[3, ] -0.037888488 -0.056591700 0.11646954 -0.0140781639 0.0185985489 -0.0191014640 - actual$covM1$Item1[4, ] 0.001390555 0.016363305 -0.01407816 0.0045346665 -0.0033525860 -0.0007070005 + expected$covM1$Item1[4, ] 0.001390555 0.016363307 -0.01407816 0.0045346669 -0.0033525866 -0.0007070006 - actual$covM1$Item1[5, ] -0.016271652 -0.020049341 0.01859855 -0.0033525860 0.0069727807 0.0005040500 + expected$covM1$Item1[5, ] -0.016271652 -0.020049344 0.01859855 -0.0033525866 0.0069727816 0.0005040511 - actual$covM1$Item1[6, ] -0.001226802 -0.009248695 -0.01910147 -0.0007070005 0.0005040500 0.0111706500 + expected$covM1$Item1[6, ] -0.001226806 -0.009248697 -0.01910146 -0.0007070006 0.0005040511 0.0111706500 actual$covM1$Item7 vs expected$covM1$Item7 [,1] [,2] [,3] [,4] [,5] [,6] - actual$covM1$Item7[1, ] 0.266226415 -0.113628960 -0.09733677 -0.174640245 0.009870489 0.013157213 + expected$covM1$Item7[1, ] 0.266226425 -0.113628963 -0.09733680 -0.174640246 0.009870488 0.013157219 - actual$covM1$Item7[2, ] -0.113628960 0.189144194 0.07184297 0.187418804 -0.065339144 0.007629050 + expected$covM1$Item7[2, ] -0.113628963 0.189144194 0.07184299 0.187418802 -0.065339143 0.007629049 - actual$covM1$Item7[3, ] -0.097336771 0.071842970 0.09207351 0.080183890 -0.012665938 -0.015228160 + expected$covM1$Item7[3, ] -0.097336804 0.071842985 0.09207352 0.080183911 -0.012665940 -0.015228159 - actual$covM1$Item7[4, ] -0.174640245 0.187418804 0.08018389 0.208602335 -0.057307789 0.004419499 + expected$covM1$Item7[4, ] -0.174640246 0.187418802 0.08018391 0.208602329 -0.057307786 0.004419496 - actual$covM1$Item7[5, ] 0.009870489 -0.065339144 -0.01266594 -0.057307789 0.028346361 -0.007015564 + expected$covM1$Item7[5, ] 0.009870488 -0.065339143 -0.01266594 -0.057307786 0.028346360 -0.007015564 - actual$covM1$Item7[6, ] 0.013157213 0.007629050 -0.01522816 0.004419499 -0.007015564 0.006370656 + expected$covM1$Item7[6, ] 0.013157219 0.007629049 -0.01522816 0.004419496 -0.007015564 0.006370655 actual$covM1$Item8 vs expected$covM1$Item8 [,1] [,2] [,3] [,4] [,5] [,6] - actual$covM1$Item8[1, ] 0.136218595 -0.001234253 -0.053715358 -0.017823004 -0.0171254833 0.0074588463 + expected$covM1$Item8[1, ] 0.136218579 -0.001234256 -0.053715336 -0.017823007 -0.0171254796 0.0074588406 - actual$covM1$Item8[2, ] -0.001234253 0.194949900 -0.011393082 0.058559684 -0.0377344370 -0.0153598130 + expected$covM1$Item8[2, ] -0.001234256 0.194949926 -0.011393077 0.058559705 -0.0377344472 -0.0153598188 - actual$covM1$Item8[3, ] -0.053715358 -0.011393082 0.099110762 -0.003505089 0.0128447999 -0.0216946963 + expected$covM1$Item8[3, ] -0.053715336 -0.011393077 0.099110747 -0.003505091 0.0128447970 -0.0216946942 - actual$covM1$Item8[4, ] -0.017823004 0.058559684 -0.003505089 0.021303209 -0.0099108824 -0.0039097096 + expected$covM1$Item8[4, ] -0.017823007 0.058559705 -0.003505091 0.021303220 -0.0099108880 -0.0039097113 - actual$covM1$Item8[5, ] -0.017125483 -0.037734437 0.012844800 -0.009910882 0.0105251695 0.0005678518 + expected$covM1$Item8[5, ] -0.017125480 -0.037734447 0.012844797 -0.009910888 0.0105251721 0.0005678539 - actual$covM1$Item8[6, ] 0.007458846 -0.015359813 -0.021694696 -0.003909710 0.0005678518 0.0079418450 + expected$covM1$Item8[6, ] 0.007458841 -0.015359819 -0.021694694 -0.003909711 0.0005678539 0.0079418455 actual$covM1$Item11 vs expected$covM1$Item11 [,1] [,2] [,3] [,4] [,5] [,6] - actual$covM1$Item11[1, ] 0.103187262 -0.0285811054 -0.019794494 -0.0495141221 -0.005389108 0.0052465669 + expected$covM1$Item11[1, ] 0.103187328 -0.0285812587 -0.019794464 -0.0495142615 -0.005389079 0.0052465622 - actual$covM1$Item11[2, ] -0.028581105 0.1344571089 -0.006164421 0.0971610979 -0.031410636 0.0007173716 + expected$covM1$Item11[2, ] -0.028581259 0.1344572434 -0.006164344 0.0971612630 -0.031410646 0.0007173332 - actual$covM1$Item11[3, ] -0.019794494 -0.0061644206 0.046681596 -0.0051219820 0.006834153 -0.0112628186 + expected$covM1$Item11[3, ] -0.019794464 -0.0061643439 0.046681573 -0.0051219438 0.006834130 -0.0112628070 - actual$covM1$Item11[4, ] -0.049514122 0.0971610979 -0.005121982 0.0814226647 -0.020164479 0.0005750417 + expected$covM1$Item11[4, ] -0.049514262 0.0971612630 -0.005121944 0.0814228427 -0.020164500 0.0005750174 - actual$covM1$Item11[5, ] -0.005389108 -0.0314106362 0.006834153 -0.0201644787 0.009658433 -0.0019029343 + expected$covM1$Item11[5, ] -0.005389079 -0.0314106460 0.006834130 -0.0201644997 0.009658431 -0.0019029235 - actual$covM1$Item11[6, ] 0.005246567 0.0007173716 -0.011262819 0.0005750417 -0.001902934 0.0035283468 + expected$covM1$Item11[6, ] 0.005246562 0.0007173332 -0.011262807 0.0005750174 -0.001902924 0.0035283427 actual$covM1$Item14 vs expected$covM1$Item14 [,1] [,2] [,3] [,4] [,5] [,6] - actual$covM1$Item14[1, ] 0.118488267 0.005535921 -0.052989676 -0.0113234928 -0.03962850 0.0230421139 + expected$covM1$Item14[1, ] 0.118488253 0.005535929 -0.052989676 -0.0113234873 -0.03962849 0.0230421137 - actual$covM1$Item14[2, ] 0.005535921 0.144056682 0.013477919 0.0445211748 -0.08143136 0.0022770389 + expected$covM1$Item14[2, ] 0.005535929 0.144056598 0.013477926 0.0445211355 -0.08143132 0.0022770391 - actual$covM1$Item14[3, ] -0.052989676 0.013477919 0.094801283 0.0050631157 0.01437575 -0.0391800641 + expected$covM1$Item14[3, ] -0.052989676 0.013477926 0.094801303 0.0050631158 0.01437575 -0.0391800695 - actual$covM1$Item14[4, ] -0.011323493 0.044521175 0.005063116 0.0162018851 -0.02224758 0.0003227620 + expected$covM1$Item14[4, ] -0.011323487 0.044521135 0.005063116 0.0162018680 -0.02224756 0.0003227622 - actual$covM1$Item14[5, ] -0.039628496 -0.081431362 0.014375754 -0.0222475791 0.05966128 -0.0118841684 + expected$covM1$Item14[5, ] -0.039628494 -0.081431316 0.014375751 -0.0222475584 0.05966125 -0.0118841686 - actual$covM1$Item14[6, ] 0.023042114 0.002277039 -0.039180064 0.0003227620 -0.01188417 0.0188360951 + expected$covM1$Item14[6, ] 0.023042114 0.002277039 -0.039180069 0.0003227622 -0.01188417 0.0188360969 actual$covM1$Item16 vs expected$covM1$Item16 [,1] [,2] [,3] [,4] [,5] [,6] - actual$covM1$Item16[1, ] 0.069536957 -0.0024262552 -0.0355827337 -7.366823e-03 -0.018663109 1.334985e-02 + expected$covM1$Item16[1, ] 0.069536906 -0.0024262422 -0.0355826899 -7.366816e-03 -0.018663099 1.334982e-02 - actual$covM1$Item16[2, ] -0.002426255 0.1009296598 0.0010289775 2.228864e-02 -0.039825988 1.697896e-04 + expected$covM1$Item16[2, ] -0.002426242 0.1009296716 0.0010289591 2.228865e-02 -0.039825988 1.697760e-04 - actual$covM1$Item16[3, ] -0.035582734 0.0010289775 0.0702255669 2.817081e-04 0.012895186 -2.629496e-02 + expected$covM1$Item16[3, ] -0.035582690 0.0010289591 0.0702255872 2.816958e-04 0.012895183 -2.629495e-02 - actual$covM1$Item16[4, ] -0.007366823 0.0222886425 0.0002817081 6.286755e-03 -0.007522316 1.681403e-05 + expected$covM1$Item16[4, ] -0.007366816 0.0222886478 0.0002816958 6.286757e-03 -0.007522319 1.681412e-05 - actual$covM1$Item16[5, ] -0.018663109 -0.0398259879 0.0128951856 -7.522316e-03 0.022889389 -6.073955e-03 + expected$covM1$Item16[5, ] -0.018663099 -0.0398259876 0.0128951835 -7.522319e-03 0.022889382 -6.073941e-03 - actual$covM1$Item16[6, ] 0.013349846 0.0001697896 -0.0262949589 1.681403e-05 -0.006073955 1.185988e-02 + expected$covM1$Item16[6, ] 0.013349819 0.0001697760 -0.0262949466 1.681412e-05 -0.006073941 1.185987e-02 actual$covM1$Item19 vs expected$covM1$Item19 [,1] [,2] [,3] [,4] [,5] [,6] - actual$covM1$Item19[1, ] 0.08600475 -0.01146327 -0.041554943 -0.014395520 -0.017346107 0.015376747 + expected$covM1$Item19[1, ] 0.08600476 -0.01146328 -0.041554942 -0.014395521 -0.017346103 0.015376744 - actual$covM1$Item19[2, ] -0.01146327 0.11937672 0.025664932 0.034086337 -0.045215648 0.015560388 + expected$covM1$Item19[2, ] -0.01146328 0.11937670 0.025664916 0.034086320 -0.045215632 0.015560386 - actual$covM1$Item19[3, ] -0.04155494 0.02566493 0.076845737 0.008457500 0.003751187 -0.018726966 + expected$covM1$Item19[3, ] -0.04155494 0.02566492 0.076845735 0.008457492 0.003751194 -0.018726967 - actual$covM1$Item19[4, ] -0.01439552 0.03408634 0.008457500 0.012158940 -0.010916097 0.003499175 + expected$covM1$Item19[4, ] -0.01439552 0.03408632 0.008457492 0.012158931 -0.010916088 0.003499173 - actual$covM1$Item19[5, ] -0.01734611 -0.04521565 0.003751187 -0.010916097 0.024341224 -0.012245019 + expected$covM1$Item19[5, ] -0.01734610 -0.04521563 0.003751194 -0.010916088 0.024341214 -0.012245016 - actual$covM1$Item19[6, ] 0.01537675 0.01556039 -0.018726966 0.003499175 -0.012245019 0.010829280 + expected$covM1$Item19[6, ] 0.01537674 0.01556039 -0.018726967 0.003499173 -0.012245016 0.010829279 actual$covM1$Item20 vs expected$covM1$Item20 [,1] [,2] [,3] [,4] [,5] [,6] - actual$covM1$Item20[1, ] 0.107586029 -0.005731491 -0.063948026 -0.0055782962 -0.030987219 0.0241259997 + expected$covM1$Item20[1, ] 0.107586034 -0.005731479 -0.063948041 -0.0055782951 -0.030987227 0.0241260057 - actual$covM1$Item20[2, ] -0.005731491 0.156676769 -0.014016515 0.0160894907 -0.065311708 -0.0043872291 + expected$covM1$Item20[2, ] -0.005731479 0.156676760 -0.014016535 0.0160894902 -0.065311711 -0.0043872209 - actual$covM1$Item20[3, ] -0.063948026 -0.014016515 0.153541322 -0.0020230453 0.030595952 -0.0579519575 + expected$covM1$Item20[3, ] -0.063948041 -0.014016535 0.153541347 -0.0020230463 0.030595966 -0.0579519675 - actual$covM1$Item20[4, ] -0.005578296 0.016089491 -0.002023045 0.0023002720 -0.005704386 -0.0002309754 + expected$covM1$Item20[4, ] -0.005578295 0.016089490 -0.002023046 0.0023002720 -0.005704386 -0.0002309750 - actual$covM1$Item20[5, ] -0.030987219 -0.065311708 0.030595952 -0.0057043856 0.040089661 -0.0091442091 + expected$covM1$Item20[5, ] -0.030987227 -0.065311711 0.030595966 -0.0057043860 0.040089666 -0.0091442146 - actual$covM1$Item20[6, ] 0.024126000 -0.004387229 -0.057951957 -0.0002309754 -0.009144209 0.0263822957 + expected$covM1$Item20[6, ] 0.024126006 -0.004387221 -0.057951967 -0.0002309750 -0.009144215 0.0263822996 ── Failure ('test-difNLR.R:134:3'): difNLR - examples at help page ───────────── Expected `fit11` to equal `fit11_expected`. Differences: actual$covM1$Item13 vs expected$covM1$Item13 [,1] [,2] [,3] [,4] [,5] - actual$covM1$Item13[1, ] 0.25126912 -0.089346410 -0.0221235270 0.0232188363 -0.105533787 + expected$covM1$Item13[1, ] 0.25126913 -0.089346412 -0.0221235275 0.0232188371 -0.105533790 - actual$covM1$Item13[2, ] -0.08934641 0.043589127 0.0046938528 -0.0191598192 0.038987025 + expected$covM1$Item13[2, ] -0.08934641 0.043589128 0.0046938531 -0.0191598194 0.038987026 - actual$covM1$Item13[3, ] -0.02212353 0.004693853 0.0172176106 0.0007736615 0.006188728 + expected$covM1$Item13[3, ] -0.02212353 0.004693853 0.0172176106 0.0007736615 0.006188729 actual$covM1$Item13[4, ] 0.02321884 -0.019159819 0.0007736615 0.0235615332 -0.010317708 - actual$covM1$Item13[5, ] -0.10553379 0.038987025 0.0061887284 -0.0103177077 0.045753707 + expected$covM1$Item13[5, ] -0.10553379 0.038987026 0.0061887286 -0.0103177080 0.045753708 ── Failure ('test-difNLR.R:142:3'): difNLR - examples at help page ───────────── Expected `fit13` to equal `fit13_expected`. Differences: actual$seM1$Item5 | expected$seM1$Item5 [1] 1.322534201 - 1.322534498 [1] [2] 0.431582728 - 0.431582819 [2] [3] 0.140846431 | 0.140846431 [3] [4] 0.140395643 | 0.140395643 [4] [5] 1.074186699 - 1.074186941 [5] actual$seM1$Item8 | expected$seM1$Item8 [1] 0.730251348 - 0.730251282 [1] [2] 0.206478511 - 0.206478465 [2] [3] 0.098993891 - 0.098993872 [3] [4] 0.148676710 - 0.148676713 [4] [5] 0.424514653 - 0.424514567 [5] `actual$seM1$Item9`: 0.55250211 0.18526841 0.09561754 0.10360347 0.29564975 `expected$seM1$Item9`: 0.55246653 0.18525882 0.09561658 0.10360309 0.29563246 actual$seM1$Item11 | expected$seM1$Item11 [1] 0.325599004 - 0.325598987 [1] [2] 0.143642509 - 0.143642500 [2] [3] 0.106457440 | 0.106457440 [3] [4] 0.116418337 | 0.116418337 [4] [5] 0.203939037 - 0.203939025 [5] actual$covM1$Item5 vs expected$covM1$Item5 [,1] [,2] [,3] [,4] [,5] - actual$covM1$Item5[1, ] 1.74909671 -0.548378108 -0.012562796 0.017270675 -1.416467759 + expected$covM1$Item5[1, ] 1.74909750 -0.548378357 -0.012562800 0.017270684 -1.416468398 - actual$covM1$Item5[2, ] -0.54837811 0.186263651 -0.004388997 -0.017203484 0.450884818 + expected$covM1$Item5[2, ] -0.54837836 0.186263730 -0.004388995 -0.017203487 0.450885020 - actual$covM1$Item5[3, ] -0.01256280 -0.004388997 0.019837717 0.009468767 0.001859731 + expected$covM1$Item5[3, ] -0.01256280 -0.004388995 0.019837717 0.009468767 0.001859734 - actual$covM1$Item5[4, ] 0.01727068 -0.017203484 0.009468767 0.019710937 -0.018236294 + expected$covM1$Item5[4, ] 0.01727068 -0.017203487 0.009468767 0.019710937 -0.018236302 - actual$covM1$Item5[5, ] -1.41646776 0.450884818 0.001859731 -0.018236294 1.153877065 + expected$covM1$Item5[5, ] -1.41646840 0.450885020 0.001859734 -0.018236302 1.153877585 actual$covM1$Item8 vs expected$covM1$Item8 [,1] [,2] [,3] [,4] [,5] - actual$covM1$Item8[1, ] 0.533267032 -0.140070238 0.0032524017 -0.0759137666 -0.308653218 + expected$covM1$Item8[1, ] 0.533266935 -0.140070190 0.0032522779 -0.0759137634 -0.308653128 - actual$covM1$Item8[2, ] -0.140070238 0.042633375 -0.0026951353 0.0148389829 0.082131213 + expected$covM1$Item8[2, ] -0.140070190 0.042633357 -0.0026951021 0.0148389789 0.082131176 - actual$covM1$Item8[3, ] 0.003252402 -0.002695135 0.0097997904 0.0004640690 -0.004603230 + expected$covM1$Item8[3, ] 0.003252278 -0.002695102 0.0097997867 0.0004640864 -0.004603157 - actual$covM1$Item8[4, ] -0.075913767 0.014838983 0.0004640690 0.0221047640 0.043974903 + expected$covM1$Item8[4, ] -0.075913763 0.014838979 0.0004640864 0.0221047650 0.043974896 - actual$covM1$Item8[5, ] -0.308653218 0.082131213 -0.0046032297 0.0439749031 0.180212690 + expected$covM1$Item8[5, ] -0.308653128 0.082131176 -0.0046031569 0.0439748964 0.180212618 actual$covM1$Item9 vs expected$covM1$Item9 [,1] [,2] [,3] [,4] [,5] - actual$covM1$Item9[1, ] 0.30525858 -0.093032991 -0.0109444470 0.0103571724 -0.162131078 + expected$covM1$Item9[1, ] 0.30521926 -0.093021165 -0.0109397238 0.0103553681 -0.162110996 - actual$covM1$Item9[2, ] -0.09303299 0.034324385 0.0015497219 -0.0086258092 0.050396087 + expected$covM1$Item9[2, ] -0.09302117 0.034320832 0.0015482627 -0.0086252624 0.050390059 - actual$covM1$Item9[3, ] -0.01094445 0.001549722 0.0091427141 0.0006446767 0.003459223 + expected$covM1$Item9[3, ] -0.01093972 0.001548263 0.0091425304 0.0006448540 0.003456701 - actual$covM1$Item9[4, ] 0.01035717 -0.008625809 0.0006446767 0.0107336796 -0.005823558 + expected$covM1$Item9[4, ] 0.01035537 -0.008625262 0.0006448540 0.0107336005 -0.005822626 - actual$covM1$Item9[5, ] -0.16213108 0.050396087 0.0034592235 -0.0058235584 0.087408777 + expected$covM1$Item9[5, ] -0.16211100 0.050390059 0.0034567009 -0.0058226256 0.087398553 actual$covM1$Item11 vs expected$covM1$Item11 [,1] [,2] [,3] [,4] [,5] - actual$covM1$Item11[1, ] 0.106014712 -0.035099245 -0.004175992 -0.004813213 -0.064548340 + expected$covM1$Item11[1, ] 0.106014700 -0.035099239 -0.004175992 -0.004813214 -0.064548332 - actual$covM1$Item11[2, ] -0.035099245 0.020633170 -0.002436880 -0.005922076 0.023791486 + expected$covM1$Item11[2, ] -0.035099239 0.020633168 -0.002436879 -0.005922076 0.023791483 - actual$covM1$Item11[3, ] -0.004175992 -0.002436880 0.011333186 0.003230759 -0.001070505 + expected$covM1$Item11[3, ] -0.004175992 -0.002436879 0.011333186 0.003230759 -0.001070505 - actual$covM1$Item11[4, ] -0.004813213 -0.005922076 0.003230759 0.013553229 0.001925740 + expected$covM1$Item11[4, ] -0.004813214 -0.005922076 0.003230759 0.013553229 0.001925740 - actual$covM1$Item11[5, ] -0.064548340 0.023791486 -0.001070505 0.001925740 0.041591131 + expected$covM1$Item11[5, ] -0.064548332 0.023791483 -0.001070505 0.001925740 0.041591126 ── Failure ('test-difNLR.R:377:3'): testing paper code - R Journal 2020 - generated data ── Expected `fit1` to equal `fit1_expected`. Differences: actual$nlrPAR[[1]] | expected$nlrPAR[[1]] [1] -1.919268840 - -1.919268809 [1] [2] 1.483505545 - 1.483505528 [2] [3] 0.000000000 | 0.000000000 [3] [4] 0.000000000 | 0.000000000 [4] [5] 0.048786386 - 0.048786382 [5] [6] 0.000000000 | 0.000000000 [6] [7] 1.000000000 | 1.000000000 [7] [8] 0.000000000 | 0.000000000 [8] actual$nlrPAR[[15]] | expected$nlrPAR[[15]] [1] 0.49475938 - 0.49475871 [1] [2] 0.87505639 - 0.87505601 [2] [3] -0.26048375 - -0.26048229 [3] [4] 0.20497607 - 0.20497701 [4] [5] 0.00000000 | 0.00000000 [5] [6] 0.00000000 | 0.00000000 [6] [7] 0.94485732 - 0.94485751 [7] [8] -0.14246026 - -0.14246067 [8] actual$nlrSE[[1]] | expected$nlrSE[[1]] [1] 0.306678421 - 0.306678419 [1] [2] 0.384450300 - 0.384450339 [2] [3] 0.000000000 | 0.000000000 [3] [4] 0.000000000 | 0.000000000 [4] [5] 0.036074443 - 0.036074445 [5] [6] 0.000000000 | 0.000000000 [6] [7] 0.187373494 - 0.187373509 [7] [8] 0.000000000 | 0.000000000 [8] actual$nlrSE[[15]] | expected$nlrSE[[15]] [1] 0.728175092 - 0.728175304 [1] [2] 0.848810046 - 0.848810111 [2] [3] 0.908598676 - 0.908598925 [3] [4] 1.146434153 - 1.146434270 [4] [5] 0.608242074 - 0.608242329 [5] [6] 0.669353204 - 0.669353421 [6] [7] 0.247191318 - 0.247191623 [7] [8] 0.304365128 - 0.304365254 [8] `actual$parM0$Item1`: -1.919268840 1.483505545 0.048786386 1.000000000 `expected$parM0$Item1`: -1.919268809 1.483505528 0.048786382 1.000000000 `actual$seM0$Item1`: 0.306678421 0.384450300 0.036074443 0.187373494 `expected$seM0$Item1`: 0.306678419 0.384450339 0.036074445 0.187373509 actual$covM0$Item1 vs expected$covM0$Item1 [,1] [,2] [,3] [,4] - actual$covM0$Item1[1, ] 0.0940516537 -0.06062457 -0.008213436 0.0005695563 + expected$covM0$Item1[1, ] 0.0940516524 -0.06062458 -0.008213436 0.0005695592 - actual$covM0$Item1[2, ] -0.0606245684 0.14780203 0.011029921 -0.0600486799 + expected$covM0$Item1[2, ] -0.0606245756 0.14780206 0.011029924 -0.0600486926 - actual$covM0$Item1[3, ] -0.0082134355 0.01102992 0.001301365 -0.0036034236 + expected$covM0$Item1[3, ] -0.0082134359 0.01102992 0.001301366 -0.0036034245 - actual$covM0$Item1[4, ] 0.0005695563 -0.06004868 -0.003603424 0.0351088262 + expected$covM0$Item1[4, ] 0.0005695592 -0.06004869 -0.003603425 0.0351088318 actual$parM1$Item14 | expected$parM1$Item14 [1] -1.471012394 - -1.471012289 [1] [2] 0.803250578 - 0.803250529 [2] [3] -0.839079694 - -0.839079901 [3] [4] 0.722644561 - 0.722644675 [4] [5] 0.094230558 - 0.094230542 [5] [6] 0.085507754 - 0.085507776 [6] [7] 1.000000000 | 1.000000000 [7] [8] 0.000000000 | 0.000000000 [8] actual$parM1$Item15 | expected$parM1$Item15 [1] 0.49475938 - 0.49475871 [1] [2] 0.87505639 - 0.87505601 [2] [3] -0.26048375 - -0.26048229 [3] [4] 0.20497607 - 0.20497701 [4] [5] 0.00000000 | 0.00000000 [5] [6] 0.00000000 | 0.00000000 [6] [7] 0.94485732 - 0.94485751 [7] [8] -0.14246026 - -0.14246067 [8] actual$seM1$Item3 | expected$seM1$Item3 [1] 0.440195514 - 0.440195511 [1] [2] 0.541812273 - 0.541812298 [2] [3] 0.680132585 - 0.680132551 [3] [4] 0.771896585 - 0.771896611 [4] [5] 0.052217411 - 0.052217412 [5] [6] 0.061351682 | 0.061351682 [6] [7] 0.451801090 - 0.451801105 [7] [8] 0.581465169 - 0.581465199 [8] actual$seM1$Item10 | expected$seM1$Item10 [1] 0.351055523 - 0.351055765 [1] [2] 0.581606548 - 0.581607061 [2] [3] 0.516361714 - 0.516361846 [3] [4] 0.823666984 - 0.823667368 [4] [5] 0.206605973 - 0.206606165 [5] [6] 0.343638938 - 0.343639068 [6] [7] 0.085395167 - 0.085395124 [7] [8] 0.123584937 - 0.123584904 [8] actual$seM1$Item14 | expected$seM1$Item14 [1] 1.135840676 - 1.135840611 [1] [2] 1.064084100 - 1.064083985 [2] [3] 1.315513227 - 1.315513180 [3] [4] 1.305080069 - 1.305079870 [4] [5] 0.190543276 - 0.190543296 [5] [6] 0.196815617 - 0.196815632 [6] [7] 1.344016238 - 1.344016126 [7] [8] 1.388639952 - 1.388639765 [8] actual$seM1$Item15 | expected$seM1$Item15 [1] 0.728175092 - 0.728175304 [1] [2] 0.848810046 - 0.848810111 [2] [3] 0.908598676 - 0.908598925 [3] [4] 1.146434153 - 1.146434270 [4] [5] 0.608242074 - 0.608242329 [5] [6] 0.669353204 - 0.669353421 [6] [7] 0.247191318 - 0.247191623 [7] [8] 0.304365128 - 0.304365254 [8] actual$covM1$Item3 vs expected$covM1$Item3 [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] - actual$covM1$Item3[1, ] 0.19377209 -0.05648306 -0.19377209 0.05648306 -0.012481388 0.012481388 -0.04274783 0.04274783 + expected$covM1$Item3[1, ] 0.19377209 -0.05648306 -0.19377209 0.05648307 -0.012481388 0.012481389 -0.04274783 0.04274783 - actual$covM1$Item3[2, ] -0.05648306 0.29356054 0.05648307 -0.29356054 0.024052291 -0.024052292 -0.21619775 0.21619775 + expected$covM1$Item3[2, ] -0.05648306 0.29356057 0.05648305 -0.29356057 0.024052293 -0.024052293 -0.21619777 0.21619777 - actual$covM1$Item3[3, ] -0.19377209 0.05648307 0.46258033 -0.22687774 0.012481389 -0.024789302 0.04274783 -0.02494488 + expected$covM1$Item3[3, ] -0.19377209 0.05648305 0.46258029 -0.22687769 0.012481387 -0.024789299 0.04274784 -0.02494490 - actual$covM1$Item3[4, ] 0.05648306 -0.29356054 -0.22687774 0.59582434 -0.024052292 0.037360618 0.21619775 -0.38386280 + expected$covM1$Item3[4, ] 0.05648307 -0.29356057 -0.22687769 0.59582438 -0.024052293 0.037360619 0.21619777 -0.38386284 actual$covM1$Item3[5, ] -0.01248139 0.02405229 0.01248139 -0.02405229 0.002726658 -0.002726658 -0.01500150 0.01500150 - actual$covM1$Item3[6, ] 0.01248139 -0.02405229 -0.02478930 0.03736062 -0.002726658 0.003764029 0.01500150 -0.02091491 + expected$covM1$Item3[6, ] 0.01248139 -0.02405229 -0.02478930 0.03736062 -0.002726658 0.003764029 0.01500150 -0.02091492 - actual$covM1$Item3[7, ] -0.04274783 -0.21619775 0.04274783 0.21619775 -0.015001496 0.015001497 0.20412422 -0.20412422 + expected$covM1$Item3[7, ] -0.04274783 -0.21619777 0.04274784 0.21619777 -0.015001498 0.015001498 0.20412424 -0.20412424 - actual$covM1$Item3[8, ] 0.04274783 0.21619775 -0.02494488 -0.38386280 0.015001496 -0.020914914 -0.20412422 0.33810174 + expected$covM1$Item3[8, ] 0.04274783 0.21619777 -0.02494490 -0.38386284 0.015001498 -0.020914916 -0.20412424 0.33810178 actual$covM1$Item7 vs expected$covM1$Item7 [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] - actual$covM1$Item7[1, ] 0.614823639 0.2293663 -0.614823638 -0.2293664 0.008075105 -0.008075124 -0.4970702 0.4970703 + expected$covM1$Item7[1, ] 0.614823642 0.2293663 -0.614823640 -0.2293664 0.008075103 -0.008075122 -0.4970702 0.4970702 - actual$covM1$Item7[2, ] 0.229366293 0.6755151 -0.229366295 -0.6755153 0.138826692 -0.138826727 -0.7547800 0.7547802 + expected$covM1$Item7[2, ] 0.229366283 0.6755151 -0.229366285 -0.6755153 0.138826691 -0.138826726 -0.7547800 0.7547801 - actual$covM1$Item7[3, ] -0.614823638 -0.2293663 0.862653820 0.2374453 -0.008075106 -0.011701539 0.4970702 -0.5971977 + expected$covM1$Item7[3, ] -0.614823640 -0.2293663 0.862653823 0.2374453 -0.008075104 -0.011701541 0.4970702 -0.5971977 - actual$covM1$Item7[4, ] -0.229366393 -0.6755153 0.237445350 1.0457479 -0.138826729 0.209479827 0.7547802 -1.0250926 + expected$covM1$Item7[4, ] -0.229366382 -0.6755153 0.237445339 1.0457479 -0.138826728 0.209479826 0.7547802 -1.0250926 - actual$covM1$Item7[5, ] 0.008075105 0.1388267 -0.008075106 -0.1388267 0.032677591 -0.032677598 -0.1403355 0.1403355 + expected$covM1$Item7[5, ] 0.008075103 0.1388267 -0.008075104 -0.1388267 0.032677591 -0.032677598 -0.1403355 0.1403355 - actual$covM1$Item7[6, ] -0.008075124 -0.1388267 -0.011701539 0.2094798 -0.032677598 0.049087804 0.1403356 -0.1855377 + expected$covM1$Item7[6, ] -0.008075122 -0.1388267 -0.011701541 0.2094798 -0.032677598 0.049087804 0.1403356 -0.1855377 - actual$covM1$Item7[7, ] -0.497070165 -0.7547800 0.497070166 0.7547802 -0.140335510 0.140335552 0.9612905 -0.9612907 + expected$covM1$Item7[7, ] -0.497070156 -0.7547800 0.497070157 0.7547802 -0.140335508 0.140335550 0.9612905 -0.9612907 - actual$covM1$Item7[8, ] 0.497070258 0.7547802 -0.597197718 -1.0250926 0.140335539 -0.185537699 -0.9612907 1.2003877 + expected$covM1$Item7[8, ] 0.497070250 0.7547801 -0.597197709 -1.0250926 0.140335537 -0.185537697 -0.9612907 1.2003877 actual$covM1$Item10 vs expected$covM1$Item10 [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] - actual$covM1$Item10[1, ] 0.123239980 0.004514857 -0.123239997 -0.004514854 -0.03078667 0.03078668 -0.013233328 0.013233328 + expected$covM1$Item10[1, ] 0.123240150 0.004514483 -0.123240156 -0.004514475 -0.03078682 0.03078682 -0.013233311 0.013233312 - actual$covM1$Item10[2, ] 0.004514857 0.338266177 -0.004514783 -0.338266080 0.10337086 -0.10337084 -0.040320605 0.040320587 + expected$covM1$Item10[2, ] 0.004514483 0.338266774 -0.004514401 -0.338266690 0.10337108 -0.10337107 -0.040320596 0.040320576 - actual$covM1$Item10[3, ] -0.123239997 -0.004514783 0.266629420 -0.003080303 0.03078670 -0.07613315 0.013233321 -0.026303372 + expected$covM1$Item10[3, ] -0.123240156 -0.004514401 0.266629556 -0.003080710 0.03078685 -0.07613330 0.013233302 -0.026303354 - actual$covM1$Item10[4, ] -0.004514854 -0.338266080 -0.003080303 0.678427300 -0.10337083 0.24605872 0.040320593 -0.083396058 + expected$covM1$Item10[4, ] -0.004514475 -0.338266690 -0.003080710 0.678427933 -0.10337106 0.24605896 0.040320585 -0.083396045 - actual$covM1$Item10[5, ] -0.030786674 0.103370863 0.030786701 -0.103370833 0.04268603 -0.04268602 -0.010016374 0.010016368 + expected$covM1$Item10[5, ] -0.030786821 0.103371082 0.030786848 -0.103371059 0.04268611 -0.04268610 -0.010016368 0.010016361 - actual$covM1$Item10[6, ] 0.030786679 -0.103370841 -0.076133149 0.246058717 -0.04268602 0.11808772 0.010016371 -0.025064191 + expected$covM1$Item10[6, ] 0.030786824 -0.103371068 -0.076133298 0.246058961 -0.04268610 0.11808781 0.010016366 -0.025064184 - actual$covM1$Item10[7, ] -0.013233328 -0.040320605 0.013233321 0.040320593 -0.01001637 0.01001637 0.007292335 -0.007292332 + expected$covM1$Item10[7, ] -0.013233311 -0.040320596 0.013233302 0.040320585 -0.01001637 0.01001637 0.007292327 -0.007292325 - actual$covM1$Item10[8, ] 0.013233328 0.040320587 -0.026303372 -0.083396058 0.01001637 -0.02506419 -0.007292332 0.015273237 + expected$covM1$Item10[8, ] 0.013233312 0.040320576 -0.026303354 -0.083396045 0.01001636 -0.02506418 -0.007292325 0.015273228 actual$covM1$Item14 vs expected$covM1$Item14 [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] - actual$covM1$Item14[1, ] 1.29013404 0.5768089 -1.29013406 -0.5768090 0.04202915 -0.04202915 -1.1245255 1.1245256 + expected$covM1$Item14[1, ] 1.29013389 0.5768087 -1.29013390 -0.5768087 0.04202913 -0.04202913 -1.1245253 1.1245253 - actual$covM1$Item14[2, ] 0.57680893 1.1322750 -0.57680896 -1.1322750 0.18988948 -0.18988948 -1.3451776 1.3451777 + expected$covM1$Item14[2, ] 0.57680872 1.1322747 -0.57680876 -1.1322746 0.18988948 -0.18988948 -1.3451774 1.3451773 - actual$covM1$Item14[3, ] -1.29013406 -0.5768090 1.73057505 0.2906895 -0.04202916 0.01751355 1.1245256 -1.1126828 + expected$covM1$Item14[3, ] -1.29013390 -0.5768088 1.73057493 0.2906891 -0.04202914 0.01751352 1.1245253 -1.1126825 - actual$covM1$Item14[4, ] -0.57680897 -1.1322750 0.29068952 1.7032340 -0.18988949 0.21828317 1.3451777 -1.5619911 + expected$covM1$Item14[4, ] -0.57680865 -1.1322746 0.29068913 1.7032335 -0.18988947 0.21828314 1.3451772 -1.5619906 - actual$covM1$Item14[5, ] 0.04202915 0.1898895 -0.04202916 -0.1898895 0.03630674 -0.03630674 -0.2049982 0.2049982 + expected$covM1$Item14[5, ] 0.04202913 0.1898895 -0.04202914 -0.1898895 0.03630675 -0.03630675 -0.2049982 0.2049982 - actual$covM1$Item14[6, ] -0.04202915 -0.1898895 0.01751355 0.2182832 -0.03630674 0.03873639 0.2049982 -0.2135040 + expected$covM1$Item14[6, ] -0.04202913 -0.1898895 0.01751352 0.2182831 -0.03630675 0.03873639 0.2049982 -0.2135040 - actual$covM1$Item14[7, ] -1.12452553 -1.3451776 1.12452558 1.3451777 -0.20499822 0.20499822 1.8063796 -1.8063797 + expected$covM1$Item14[7, ] -1.12452529 -1.3451774 1.12452534 1.3451772 -0.20499822 0.20499821 1.8063793 -1.8063793 - actual$covM1$Item14[8, ] 1.12452556 1.3451777 -1.11268281 -1.5619911 0.20499822 -0.21350402 -1.8063797 1.9283209 + expected$covM1$Item14[8, ] 1.12452525 1.3451773 -1.11268248 -1.5619906 0.20499821 -0.21350400 -1.8063793 1.9283204 actual$covM1$Item15 vs expected$covM1$Item15 [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] - actual$covM1$Item15[1, ] 0.53023897 -0.2644581 -0.530238888 0.2644581 -0.3092353 0.3092353 0.01617559 -0.016175610 + expected$covM1$Item15[1, ] 0.53023927 -0.2644582 -0.530239287 0.2644583 -0.3092356 0.3092356 0.01617564 -0.016175658 - actual$covM1$Item15[2, ] -0.26445808 0.7204785 0.264458013 -0.7204785 0.4833455 -0.4833455 -0.19288761 0.192887623 + expected$covM1$Item15[2, ] -0.26445820 0.7204786 0.264458183 -0.7204786 0.4833458 -0.4833458 -0.19288788 0.192887899 - actual$covM1$Item15[3, ] -0.53023889 0.2644580 0.825551555 -0.3536824 0.3092353 -0.3924074 -0.01617558 -0.002780452 + expected$covM1$Item15[3, ] -0.53023929 0.2644582 0.825552007 -0.3536826 0.3092355 -0.3924076 -0.01617563 -0.002780406 - actual$covM1$Item15[4, ] 0.26445811 -0.7204785 -0.353682443 1.3143113 -0.4833455 0.6797859 0.19288760 -0.313304948 + expected$covM1$Item15[4, ] 0.26445825 -0.7204786 -0.353682554 1.3143115 -0.4833458 0.6797861 0.19288787 -0.313305071 - actual$covM1$Item15[5, ] -0.30923534 0.4833455 0.309235279 -0.4833455 0.3699584 -0.3699584 -0.11567040 0.115670409 + expected$covM1$Item15[5, ] -0.30923555 0.4833458 0.309235547 -0.4833458 0.3699587 -0.3699587 -0.11567060 0.115670621 - actual$covM1$Item15[6, ] 0.30923533 -0.4833455 -0.392407368 0.6797859 -0.3699584 0.4480337 0.11567039 -0.150130266 + expected$covM1$Item15[6, ] 0.30923557 -0.4833458 -0.392407610 0.6797861 -0.3699587 0.4480340 0.11567061 -0.150130417 - actual$covM1$Item15[7, ] 0.01617559 -0.1928876 -0.016175581 0.1928876 -0.1156704 0.1156704 0.06110355 -0.061103549 + expected$covM1$Item15[7, ] 0.01617564 -0.1928879 -0.016175634 0.1928879 -0.1156706 0.1156706 0.06110370 -0.061103703 - actual$covM1$Item15[8, ] -0.01617561 0.1928876 -0.002780452 -0.3133049 0.1156704 -0.1501303 -0.06110355 0.092638131 + expected$covM1$Item15[8, ] -0.01617566 0.1928879 -0.002780406 -0.3133051 0.1156706 -0.1501304 -0.06110370 0.092638208 ── Failure ('test-difNLR.R:627:3'): testing paper code - R Journal 2020 - special cases (not included) ── Expected `fit12b` to equal `fit12b_expected`. Differences: actual$nlrPAR[[8]] | expected$nlrPAR[[8]] [1] -1.473002929 - -1.473002943 [1] [2] 1.651804345 - 1.651803928 [2] [3] -0.848182294 - -0.848182273 [3] [4] -0.360238142 - -0.360237731 [4] [5] 0.038413515 - 0.038413500 [5] [6] -0.038413515 - -0.038413500 [6] [7] 0.595815199 - 0.595815294 [7] [8] 0.075980853 - 0.075980758 [8] actual$nlrSE[[8]] | expected$nlrSE[[8]] [1] 0.808569956 - 0.808569903 [1] [2] 1.178169245 - 1.178169081 [2] [3] 1.279002730 - 1.279002650 [3] [4] 1.615576373 - 1.615576221 [4] [5] 0.073752562 - 0.073752582 [5] [6] 0.098389886 - 0.098389898 [6] [7] 0.239957143 - 0.239957276 [7] [8] 0.629422768 - 0.629422823 [8] `actual$parM0$Item3`: -2.675261061 1.244052278 0.012796029 1.000000000 `expected$parM0$Item3`: -2.675261125 1.244052309 0.012796033 1.000000000 `actual$seM0$Item3`: 0.614646256 0.667965478 0.044831991 0.751300809 `expected$seM0$Item3`: 0.614646232 0.667965449 0.044831989 0.751300770 `actual$seM0$Item11`: 0.563259467 0.797881387 0.586945683 0.068677472 `expected$seM0$Item11`: 0.563259429 0.797881417 0.586945703 0.068677471 actual$covM0$Item3 vs expected$covM0$Item3 [,1] [,2] [,3] [,4] - actual$covM0$Item3[1, ] 0.37779002 -0.03882075 -0.011412620 -0.16276770 + expected$covM0$Item3[1, ] 0.37778999 -0.03882075 -0.011412619 -0.16276770 - actual$covM0$Item3[2, ] -0.03882075 0.44617788 0.025013290 -0.44524259 + expected$covM0$Item3[2, ] -0.03882075 0.44617784 0.025013287 -0.44524254 - actual$covM0$Item3[3, ] -0.01141262 0.02501329 0.002009907 -0.02115141 + expected$covM0$Item3[3, ] -0.01141262 0.02501329 0.002009907 -0.02115140 - actual$covM0$Item3[4, ] -0.16276770 -0.44524259 -0.021151406 0.56445290 + expected$covM0$Item3[4, ] -0.16276770 -0.44524254 -0.021151403 0.56445285 actual$covM0$Item11 vs expected$covM0$Item11 [,1] [,2] [,3] [,4] - actual$covM0$Item11[1, ] 0.31726123 -0.05669515 -0.16785082 -0.011100733 + expected$covM0$Item11[1, ] 0.31726118 -0.05669517 -0.16785082 -0.011100729 - actual$covM0$Item11[2, ] -0.05669515 0.63661471 0.42438104 -0.044653974 + expected$covM0$Item11[2, ] -0.05669517 0.63661475 0.42438108 -0.044653976 - actual$covM0$Item11[3, ] -0.16785082 0.42438104 0.34450524 -0.024960115 + expected$covM0$Item11[3, ] -0.16785082 0.42438108 0.34450526 -0.024960118 - actual$covM0$Item11[4, ] -0.01110073 -0.04465397 -0.02496011 0.004716595 + expected$covM0$Item11[4, ] -0.01110073 -0.04465398 -0.02496012 0.004716595 actual$parM1$Item1 | expected$parM1$Item1 [1] -1.189848991 - -1.189848984 [1] [2] 1.145283434 - 1.145283431 [2] [3] -0.817141498 - -0.817141592 [3] [4] 0.723674493 - 0.723674568 [4] [5] 0.000000000 | 0.000000000 [5] [6] 0.062796987 - 0.062796995 [6] [7] 1.000000000 | 1.000000000 [7] [8] 0.000000000 | 0.000000000 [8] actual$parM1$Item4 | expected$parM1$Item4 [1] -1.567209923 - -1.567209311 [1] [2] 1.903818995 - 1.903817654 [2] [3] 1.011419581 - 1.011419036 [3] [4] -0.062105562 - -0.062104333 [4] [5] 0.112839514 - 0.112839428 [5] [6] -0.112839514 - -0.112839428 [6] [7] 0.971993138 - 0.971993376 [7] [8] -0.036729069 - -0.036729310 [8] actual$parM1$Item8 | expected$parM1$Item8 [1] -1.473002929 - -1.473002943 [1] [2] 1.651804345 - 1.651803928 [2] [3] -0.848182294 - -0.848182273 [3] [4] -0.360238142 - -0.360237731 [4] [5] 0.038413515 - 0.038413500 [5] [6] -0.038413515 - -0.038413500 [6] [7] 0.595815199 - 0.595815294 [7] [8] 0.075980853 - 0.075980758 [8] actual$seM1$Item1 | expected$seM1$Item1 [1] 0.670888292 - 0.670888296 [1] [2] 0.914399661 - 0.914399690 [2] [3] 0.919702014 - 0.919702008 [3] [4] 1.205226878 - 1.205226952 [4] [5] 0.162430339 - 0.162430342 [5] [6] 0.173488584 - 0.173488588 [6] [7] 0.587907036 - 0.587907052 [7] [8] 0.631979686 - 0.631979699 [8] actual$seM1$Item4 | expected$seM1$Item4 [1] 0.642819527 - 0.642819304 [1] [2] 0.961230077 - 0.961229614 [2] [3] 0.762108561 - 0.762108396 [3] [4] 1.213841240 - 1.213840174 [4] [5] 0.078494417 - 0.078494480 [5] [6] 0.125287868 - 0.125287890 [6] [7] 0.234655852 - 0.234656108 [7] [8] 0.271485350 - 0.271485545 [8] actual$seM1$Item8 | expected$seM1$Item8 [1] 0.808569956 - 0.808569903 [1] [2] 1.178169245 - 1.178169081 [2] [3] 1.279002730 - 1.279002650 [3] [4] 1.615576373 - 1.615576221 [4] [5] 0.073752562 - 0.073752582 [5] [6] 0.098389886 - 0.098389898 [6] [7] 0.239957143 - 0.239957276 [7] [8] 0.629422768 - 0.629422823 [8] actual$covM1$Item1 vs expected$covM1$Item1 [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] - actual$covM1$Item1[1, ] 0.45009110 -0.04294095 -0.45009111 0.04294097 -0.04683519 0.04683519 -0.13218541 0.13218540 + expected$covM1$Item1[1, ] 0.45009111 -0.04294095 -0.45009110 0.04294095 -0.04683519 0.04683519 -0.13218541 0.13218541 - actual$covM1$Item1[2, ] -0.04294095 0.83612674 0.04294093 -0.83612674 0.13124319 -0.13124319 -0.48351911 0.48351911 + expected$covM1$Item1[2, ] -0.04294095 0.83612679 0.04294097 -0.83612682 0.13124320 -0.13124320 -0.48351914 0.48351914 - actual$covM1$Item1[3, ] -0.45009111 0.04294093 0.84585180 -0.38087451 0.04683519 -0.07680061 0.13218542 -0.11288995 + expected$covM1$Item1[3, ] -0.45009110 0.04294097 0.84585178 -0.38087457 0.04683519 -0.07680061 0.13218540 -0.11288993 - actual$covM1$Item1[4, ] 0.04294097 -0.83612674 -0.38087451 1.45257183 -0.13124319 0.16413812 0.48351911 -0.62169505 + expected$covM1$Item1[4, ] 0.04294095 -0.83612682 -0.38087457 1.45257200 -0.13124320 0.16413813 0.48351916 -0.62169511 - actual$covM1$Item1[5, ] -0.04683519 0.13124319 0.04683519 -0.13124319 0.02638361 -0.02638361 -0.06496814 0.06496814 + expected$covM1$Item1[5, ] -0.04683519 0.13124320 0.04683519 -0.13124320 0.02638362 -0.02638362 -0.06496814 0.06496814 - actual$covM1$Item1[6, ] 0.04683519 -0.13124319 -0.07680061 0.16413812 -0.02638361 0.03009829 0.06496814 -0.07041179 + expected$covM1$Item1[6, ] 0.04683519 -0.13124320 -0.07680061 0.16413813 -0.02638362 0.03009829 0.06496814 -0.07041180 - actual$covM1$Item1[7, ] -0.13218541 -0.48351911 0.13218542 0.48351911 -0.06496814 0.06496814 0.34563468 -0.34563468 + expected$covM1$Item1[7, ] -0.13218541 -0.48351914 0.13218540 0.48351916 -0.06496814 0.06496814 0.34563470 -0.34563470 - actual$covM1$Item1[8, ] 0.13218540 0.48351911 -0.11288995 -0.62169505 0.06496814 -0.07041179 -0.34563468 0.39939832 + expected$covM1$Item1[8, ] 0.13218541 0.48351914 -0.11288993 -0.62169511 0.06496814 -0.07041180 -0.34563470 0.39939834 actual$covM1$Item4 vs expected$covM1$Item4 [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] - actual$covM1$Item4[1, ] 0.413216944 -0.35539902 -0.413216941 0.35539899 -0.039550690 0.039550686 0.008273772 -0.008273776 + expected$covM1$Item4[1, ] 0.413216657 -0.35539845 -0.413216667 0.35539821 -0.039550703 0.039550689 0.008273723 -0.008273715 - actual$covM1$Item4[2, ] -0.355399016 0.92396326 0.355398995 -0.92396316 0.056609039 -0.056609025 -0.179534054 0.179534064 + expected$covM1$Item4[2, ] -0.355398450 0.92396237 0.355398486 -0.92396174 0.056609075 -0.056609036 -0.179534198 0.179534178 - actual$covM1$Item4[3, ] -0.413216941 0.35539900 0.580809459 -0.45634154 0.039550690 -0.066430525 -0.008273767 -0.004042090 + expected$covM1$Item4[3, ] -0.413216667 0.35539849 0.580809207 -0.45634078 0.039550705 -0.066430533 -0.008273730 -0.004042139 - actual$covM1$Item4[4, ] 0.355398986 -0.92396316 -0.456341540 1.47341056 -0.056609033 0.111534391 0.179534031 -0.255284750 + expected$covM1$Item4[4, ] 0.355398213 -0.92396174 -0.456340783 1.47340797 -0.056609036 0.111534329 0.179534074 -0.255284706 - actual$covM1$Item4[5, ] -0.039550690 0.05660904 0.039550690 -0.05660903 0.006161374 -0.006161373 -0.008446771 0.008446772 + expected$covM1$Item4[5, ] -0.039550703 0.05660907 0.039550705 -0.05660904 0.006161383 -0.006161381 -0.008446793 0.008446792 - actual$covM1$Item4[6, ] 0.039550686 -0.05660903 -0.066430525 0.11153439 -0.006161373 0.015697050 0.008446769 -0.014274250 + expected$covM1$Item4[6, ] 0.039550689 -0.05660904 -0.066430533 0.11153433 -0.006161381 0.015697055 0.008446786 -0.014274262 - actual$covM1$Item4[7, ] 0.008273772 -0.17953405 -0.008273767 0.17953403 -0.008446771 0.008446769 0.055063369 -0.055063371 + expected$covM1$Item4[7, ] 0.008273723 -0.17953420 -0.008273730 0.17953407 -0.008446793 0.008446786 0.055063489 -0.055063485 - actual$covM1$Item4[8, ] -0.008273776 0.17953406 -0.004042090 -0.25528475 0.008446772 -0.014274250 -0.055063371 0.073704295 + expected$covM1$Item4[8, ] -0.008273715 0.17953418 -0.004042139 -0.25528471 0.008446792 -0.014274262 -0.055063485 0.073704401 actual$covM1$Item8 vs expected$covM1$Item8 [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] - actual$covM1$Item8[1, ] 0.653785373 -0.44925205 -0.653785394 0.44925206 -0.044383938 0.044383939 -0.005709065 0.005709062 + expected$covM1$Item8[1, ] 0.653785288 -0.44925180 -0.653785274 0.44925181 -0.044383941 0.044383942 -0.005709095 0.005709083 - actual$covM1$Item8[2, ] -0.449252053 1.38808277 0.449252097 -1.38808280 0.068811546 -0.068811549 -0.234050242 0.234050264 + expected$covM1$Item8[2, ] -0.449251797 1.38808238 0.449251736 -1.38808240 0.068811560 -0.068811561 -0.234050353 0.234050386 - actual$covM1$Item8[3, ] -0.653785394 0.44925210 1.635847984 -0.89312052 0.044383940 -0.087214992 0.005709058 -0.055455606 + expected$covM1$Item8[3, ] -0.653785274 0.44925174 1.635847779 -0.89312006 0.044383938 -0.087214985 0.005709106 -0.055455691 - actual$covM1$Item8[4, ] 0.449252059 -1.38808280 -0.893120524 2.61008702 -0.068811547 0.126921855 0.234050248 -0.784526730 + expected$covM1$Item8[4, ] 0.449251805 -1.38808240 -0.893120064 2.61008653 -0.068811561 0.126921862 0.234050355 -0.784526835 - actual$covM1$Item8[5, ] -0.044383938 0.06881155 0.044383940 -0.06881155 0.005439440 -0.005439441 -0.009165172 0.009165173 + expected$covM1$Item8[5, ] -0.044383941 0.06881156 0.044383938 -0.06881156 0.005439443 -0.005439443 -0.009165181 0.009165183 - actual$covM1$Item8[6, ] 0.044383939 -0.06881155 -0.087214992 0.12692186 -0.005439441 0.009680570 0.009165173 -0.030676575 + expected$covM1$Item8[6, ] 0.044383942 -0.06881156 -0.087214985 0.12692186 -0.005439443 0.009680572 0.009165181 -0.030676583 - actual$covM1$Item8[7, ] -0.005709065 -0.23405024 0.005709058 0.23405025 -0.009165172 0.009165173 0.057579431 -0.057579435 + expected$covM1$Item8[7, ] -0.005709095 -0.23405035 0.005709106 0.23405035 -0.009165181 0.009165181 0.057579494 -0.057579500 - actual$covM1$Item8[8, ] 0.005709062 0.23405026 -0.055455606 -0.78452673 0.009165173 -0.030676575 -0.057579435 0.396173021 + expected$covM1$Item8[8, ] 0.005709083 0.23405039 -0.055455691 -0.78452683 0.009165183 -0.030676583 -0.057579500 0.396173090 ── Failure ('test-difORD.R:14:3'): difORD - examples at help page ────────────── Expected `fit1` to equal `fit1_expected`. Differences: actual$ordPAR$R24 | expected$ordPAR$R24 [1] 0.067726993 - 0.067726984 [1] [2] -1.295691816 - -1.295691792 [2] [3] -3.713274816 - -3.713274756 [3] [4] -5.405622049 - -5.405621944 [4] [5] 1.824596604 - 1.824596577 [5] actual$parM0$R24 | expected$parM0$R24 [1] 0.067726993 - 0.067726984 [1] [2] -1.295691816 - -1.295691792 [2] [3] -3.713274816 - -3.713274756 [3] [4] -5.405622049 - -5.405621944 [4] [5] 1.824596604 - 1.824596577 [5] actual$parM1$R5 | expected$parM1$R5 [1] -1.893765319 - -1.893765389 [1] [2] -1.392489884 - -1.392490077 [2] [3] -4.199838848 - -4.199839090 [3] [4] -4.752433837 - -4.752434170 [4] [5] 1.577181608 - 1.577181728 [5] [6] 0.126040241 - 0.126040306 [6] [7] -0.147234071 - -0.147234119 [7] actual$parM1$R17 | expected$parM1$R17 [1] -2.859095215 - -2.859095114 [1] [2] -4.474701323 - -4.474701157 [2] [3] -6.617934018 - -6.617933791 [3] [4] -7.683710538 - -7.683710147 [4] [5] 1.973534320 - 1.973534245 [5] [6] 0.391732910 - 0.391732843 [6] [7] -0.103073650 - -0.103073618 [7] ── Failure ('test-difORD.R:49:3'): difORD - examples at help page ────────────── Expected `fit2` to equal `fit2_expected`. Differences: actual$ordPAR$R24 | expected$ordPAR$R24 [1] 0.067726993 - 0.067726984 [1] [2] -1.295691816 - -1.295691792 [2] [3] -3.713274816 - -3.713274756 [3] [4] -5.405622049 - -5.405621944 [4] [5] 1.824596604 - 1.824596577 [5] actual$parM0$R24 | expected$parM0$R24 [1] 0.067726993 - 0.067726984 [1] [2] -1.295691816 - -1.295691792 [2] [3] -3.713274816 - -3.713274756 [3] [4] -5.405622049 - -5.405621944 [4] [5] 1.824596604 - 1.824596577 [5] actual$parM1$R5 | expected$parM1$R5 [1] -1.893765319 - -1.893765389 [1] [2] -1.392489884 - -1.392490077 [2] [3] -4.199838848 - -4.199839090 [3] [4] -4.752433837 - -4.752434170 [4] [5] 1.577181608 - 1.577181728 [5] [6] 0.126040241 - 0.126040306 [6] [7] -0.147234071 - -0.147234119 [7] actual$parM1$R17 | expected$parM1$R17 [1] -2.859095215 - -2.859095114 [1] [2] -4.474701323 - -4.474701157 [2] [3] -6.617934018 - -6.617933791 [3] [4] -7.683710538 - -7.683710147 [4] [5] 1.973534320 - 1.973534245 [5] [6] 0.391732910 - 0.391732843 [6] [7] -0.103073650 - -0.103073618 [7] ── Failure ('test-difORD.R:55:3'): difORD - examples at help page ────────────── Expected `fit3` to equal `fit3_expected`. Differences: actual$parM1$R28 | expected$parM1$R28 [1] 0.412218785 - 0.412218766 [1] [2] -0.482838864 - -0.482838851 [2] [3] -3.206750000 - -3.206749935 [3] [4] -6.095092281 - -6.095092076 [4] [5] 1.949128977 - 1.949128942 [5] [6] 0.220172658 - 0.220172657 [6] [7] -0.108002478 - -0.108002479 [7] ── Failure ('test-difORD.R:61:3'): difORD - examples at help page ────────────── Expected `fit4` to equal `fit4_expected`. Differences: actual$ordPAR$R24 | expected$ordPAR$R24 [1] 0.067726993 - 0.067726984 [1] [2] -1.295691816 - -1.295691792 [2] [3] -3.713274816 - -3.713274756 [3] [4] -5.405622049 - -5.405621944 [4] [5] 1.824596604 - 1.824596577 [5] actual$parM0$R24 | expected$parM0$R24 [1] 0.067726993 - 0.067726984 [1] [2] -1.295691816 - -1.295691792 [2] [3] -3.713274816 - -3.713274756 [3] [4] -5.405622049 - -5.405621944 [4] [5] 1.824596604 - 1.824596577 [5] ── Failure ('test-difORD.R:67:3'): difORD - examples at help page ────────────── Expected `fit5` to equal `fit5_expected`. Differences: actual$parM1$R5 | expected$parM1$R5 [1] -1.893765319 - -1.893765389 [1] [2] -1.392489884 - -1.392490077 [2] [3] -4.199838848 - -4.199839090 [3] [4] -4.752433837 - -4.752434170 [4] [5] 1.577181608 - 1.577181728 [5] [6] 0.126040241 - 0.126040306 [6] [7] -0.147234071 - -0.147234119 [7] actual$parM1$R17 | expected$parM1$R17 [1] -2.859095215 - -2.859095114 [1] [2] -4.474701323 - -4.474701157 [2] [3] -6.617934018 - -6.617933791 [3] [4] -7.683710538 - -7.683710147 [4] [5] 1.973534320 - 1.973534245 [5] [6] 0.391732910 - 0.391732843 [6] [7] -0.103073650 - -0.103073618 [7] ── Failure ('test-difORD.R:73:3'): difORD - examples at help page ────────────── Expected `fit6` to equal `fit6_expected`. Differences: actual$ordPAR$R28 | expected$ordPAR$R28 [1] -4.121246755 - -4.121246690 [1] [2] -4.950121086 - -4.950120991 [2] [3] -7.704178990 - -7.704178841 [3] [4] -10.667119450 - -10.667119165 [4] [5] 0.093639175 - 0.093639173 [5] actual$parM0$R20 | expected$parM0$R20 [1] -5.683252711 - -5.683253024 [1] [2] -6.437898816 - -6.437899279 [2] [3] -8.982539929 - -8.982540492 [3] [4] -10.651801297 - -10.651801994 [4] [5] 0.091752686 - 0.091752692 [5] actual$parM0$R28 | expected$parM0$R28 [1] -4.121246755 - -4.121246690 [1] [2] -4.950121086 - -4.950120991 [2] [3] -7.704178990 - -7.704178841 [3] [4] -10.667119450 - -10.667119165 [4] [5] 0.093639175 - 0.093639173 [5] ── Failure ('test-estimNLR.R:96:3'): estimNLR - examples at help page ────────── Expected `fit_irls` to equal `fit_irls_expected`. Differences: environment(actual$family$variance)$okLinks[3:6] vs environment(expected$family$variance)$okLinks[3:5] "cloglog" "cauchit" "log" - "identity" [ FAIL 24 | WARN 1 | SKIP 16 | PASS 318 ] Deleting unused snapshots: 'difNLR/plot-fit1-gen.svg', 'difNLR/plot-fit2-gen.svg', and 'difNLR/plot-stat-gen.svg' Error: ! Test failures. Execution halted Package: digitize Check: tests New result: ERROR Running ‘testthat.R’ [1s/1s] Running the tests in ‘tests/testthat.R’ failed. Complete output: > library(testthat) > library(digitize) > > test_check("digitize") Saving _problems/test-reverse_compatible-38.R Saving _problems/test-unit-15.R [ FAIL 2 | WARN 0 | SKIP 0 | PASS 1 ] ══ Failed tests ════════════════════════════════════════════════════════════════ ── Error ('test-reverse_compatible.r:32:13'): `digitize` gives same ──────────── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test-reverse_compatible.r:32:13 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) ── Error ('test-unit.r:9:13'): Digitize skips point input ────────────────────── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test-unit.r:9:13 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) [ FAIL 2 | WARN 0 | SKIP 0 | PASS 1 ] Error: ! Test failures. Execution halted Package: distances Check: tests New result: ERROR Running ‘testthat.R’ [4s/4s] Running the tests in ‘tests/testthat.R’ failed. Complete output: > library(testthat) > library(distances) > > test_check("distances") Saving _problems/test_input_check-47.R Saving _problems/test_input_check-70.R Saving _problems/test_input_check-86.R Saving _problems/test_input_check-123.R Saving _problems/test_input_check-164.R Saving _problems/test_input_check-251.R Saving _problems/test_input_check-275.R Saving _problems/test_input_check-302.R [ FAIL 8 | WARN 0 | SKIP 0 | PASS 419 ] ══ Failed tests ════════════════════════════════════════════════════════════════ ── Error ('test_input_check.R:45:3'): `new_error` & `new_warning` make warnings and errors. ── Error in `expect_error(t_new_error("This is an error."), class = c("error", "condition"), regexp = "This is an error.")`: `class` must be a single string or `NULL`, not a character vector. Backtrace: ▆ 1. └─testthat::expect_error(class = c("error", "condition")) at test_input_check.R:45:3 2. └─testthat:::check_string(class, allow_null = TRUE) 3. └─testthat:::stop_input_type(...) 4. └─rlang::abort(message, ..., call = call, arg = arg) ── Error ('test_input_check.R:68:3'): `ensure_distances` checks input. ───────── Error in `expect_error(t_ensure_distances(t_distances = "a"), class = c("error", "condition"), regexp = "`t_distances` is not a `distances` object.")`: `class` must be a single string or `NULL`, not a character vector. Backtrace: ▆ 1. └─testthat::expect_error(class = c("error", "condition")) at test_input_check.R:68:3 2. └─testthat:::check_string(class, allow_null = TRUE) 3. └─testthat:::stop_input_type(...) 4. └─rlang::abort(message, ..., call = call, arg = arg) ── Error ('test_input_check.R:85:3'): `coerce_args` checks input. ────────────── Error in `expect_error(t_coerce_args(t_choices = 1L), class = c("error", "condition"))`: `class` must be a single string or `NULL`, not a character vector. Backtrace: ▆ 1. └─testthat::expect_error(class = c("error", "condition")) at test_input_check.R:85:3 2. └─testthat:::check_string(class, allow_null = TRUE) 3. └─testthat:::stop_input_type(...) 4. └─rlang::abort(message, ..., call = call, arg = arg) ── Error ('test_input_check.R:121:3'): `coerce_character` checks input. ──────── Error in `expect_error(t_coerce_character(t_req_length = 8), class = c("error", "condition"), regexp = "`t_x` is not of length `t_req_length`.")`: `class` must be a single string or `NULL`, not a character vector. Backtrace: ▆ 1. └─testthat::expect_error(class = c("error", "condition")) at test_input_check.R:121:3 2. └─testthat:::check_string(class, allow_null = TRUE) 3. └─testthat:::stop_input_type(...) 4. └─rlang::abort(message, ..., call = call, arg = arg) ── Error ('test_input_check.R:162:3'): `coerce_distance_data` checks input. ──── Error in `expect_error(t_coerce_distance_data(t_data = dist(1:10)), class = c("error", "condition"), regexp = "`t_data` must be vector, matrix or data frame.")`: `class` must be a single string or `NULL`, not a character vector. Backtrace: ▆ 1. └─testthat::expect_error(class = c("error", "condition")) at test_input_check.R:162:3 2. └─testthat:::check_string(class, allow_null = TRUE) 3. └─testthat:::stop_input_type(...) 4. └─rlang::abort(message, ..., call = call, arg = arg) ── Error ('test_input_check.R:249:3'): `coerce_double` checks input. ─────────── Error in `expect_error(t_coerce_double(t_x = letters[1:10]), class = c("error", "condition"), regexp = "`t_x` must be double or NULL.")`: `class` must be a single string or `NULL`, not a character vector. Backtrace: ▆ 1. └─testthat::expect_error(class = c("error", "condition")) at test_input_check.R:249:3 2. └─testthat:::check_string(class, allow_null = TRUE) 3. └─testthat:::stop_input_type(...) 4. └─rlang::abort(message, ..., call = call, arg = arg) ── Error ('test_input_check.R:273:3'): `coerce_integer` checks input. ────────── Error in `expect_error(t_coerce_integer(t_x = letters[1:10]), class = c("error", "condition"), regexp = "`t_x` must be integer or NULL.")`: `class` must be a single string or `NULL`, not a character vector. Backtrace: ▆ 1. └─testthat::expect_error(class = c("error", "condition")) at test_input_check.R:273:3 2. └─testthat:::check_string(class, allow_null = TRUE) 3. └─testthat:::stop_input_type(...) 4. └─rlang::abort(message, ..., call = call, arg = arg) ── Error ('test_input_check.R:300:3'): `coerce_norm_matrix` checks input. ────── Error in `expect_error(t_coerce_norm_matrix(t_mat = dist(1:4)), class = c("error", "condition"), regexp = "`t_mat` must be matrix, data.frame or vector.")`: `class` must be a single string or `NULL`, not a character vector. Backtrace: ▆ 1. └─testthat::expect_error(class = c("error", "condition")) at test_input_check.R:300:3 2. └─testthat:::check_string(class, allow_null = TRUE) 3. └─testthat:::stop_input_type(...) 4. └─rlang::abort(message, ..., call = call, arg = arg) [ FAIL 8 | WARN 0 | SKIP 0 | PASS 419 ] Error: ! Test failures. Execution halted Package: dmlalg Check: tests New result: ERROR Running ‘testthat.R’ [49s/49s] Running the tests in ‘tests/testthat.R’ failed. Complete output: > library(testthat) > library(dmlalg) > > test_check("dmlalg") Saving _problems/test-data_formats_regsdml-1332.R [ FAIL 1 | WARN 0 | SKIP 0 | PASS 1113 ] ══ Failed tests ════════════════════════════════════════════════════════════════ ── Error ('test-data_formats_regsdml.R:1332:5'): return_results outputs right data formats ── Error in `expect(nrow(results$beta_test), d)`: `ok` must be `TRUE` or `FALSE`, not the number 3. Backtrace: ▆ 1. └─dmlalg (local) check_return_results_format(beta = beta, var = var, xx_colnames = xx_colnames) at test-data_formats_regsdml.R:1356:3 2. └─testthat::expect(ok = nrow(results$beta_test)) at test-data_formats_regsdml.R:1332:5 3. └─testthat:::check_bool(ok) 4. └─testthat:::stop_input_type(...) 5. └─rlang::abort(message, ..., call = call, arg = arg) [ FAIL 1 | WARN 0 | SKIP 0 | PASS 1113 ] Error: ! Test failures. Execution halted Package: DSMolgenisArmadillo Check: tests New result: ERROR Running ‘testthat.R’ [9s/9s] Running the tests in ‘tests/testthat.R’ failed. Complete output: > library(testthat) > library(DSMolgenisArmadillo) Loading required package: DSI Loading required package: progress Loading required package: R6 Loading required package: MolgenisAuth > library(tibble) > > test_check("DSMolgenisArmadillo") Saving _problems/test-ArmadilloConnection-7.R Saving _problems/test-ArmadilloConnection-21.R Saving _problems/test-ArmadilloConnection-40.R Saving _problems/test-ArmadilloConnection-57.R Saving _problems/test-ArmadilloConnection-77.R Saving _problems/test-ArmadilloConnection-94.R Saving _problems/test-ArmadilloConnection-108.R Saving _problems/test-ArmadilloConnection-122.R Saving _problems/test-ArmadilloConnection-136.R Saving _problems/test-ArmadilloConnection-150.R Saving _problems/test-ArmadilloConnection-179.R Saving _problems/test-ArmadilloConnection-193.R Saving _problems/test-ArmadilloConnection-206.R Saving _problems/test-ArmadilloConnection-229.R Saving _problems/test-ArmadilloConnection-254.R Saving _problems/test-ArmadilloConnection-273.R Saving _problems/test-ArmadilloConnection-297.R Saving _problems/test-ArmadilloConnection-330.R Saving _problems/test-ArmadilloConnection-369.R Saving _problems/test-ArmadilloConnection-400.R Saving _problems/test-ArmadilloConnection-424.R Saving _problems/test-ArmadilloConnection-440.R Saving _problems/test-ArmadilloConnection-455.R Saving _problems/test-ArmadilloConnection-474.R Saving _problems/test-ArmadilloConnection-495.R Saving _problems/test-ArmadilloConnection-521.R Saving _problems/test-ArmadilloConnection-544.R Saving _problems/test-ArmadilloConnection-578.R Saving _problems/test-ArmadilloConnection-606.R Saving _problems/test-ArmadilloConnection-629.R Saving _problems/test-ArmadilloDriver-29.R Saving _problems/test-ArmadilloDriver-60.R Saving _problems/test-ArmadilloDriver-90.R Saving _problems/test-ArmadilloDriver-119.R Saving _problems/test-ArmadilloDriver-150.R Saving _problems/test-ArmadilloOAuth-14.R Saving _problems/test-ArmadilloOAuth-48.R Saving _problems/test-ArmadilloOAuth-106.R Saving _problems/test-ArmadilloOAuth-167.R Saving _problems/test-ArmadilloOAuth-202.R Saving _problems/test-ArmadilloOAuth-231.R Saving _problems/test-ArmadilloOAuth-272.R Saving _problems/test-ArmadilloOAuth-301.R Saving _problems/test-ArmadilloOAuth-315.R Saving _problems/test-ArmadilloOAuth-335.R Saving _problems/test-ArmadilloOAuth-1038.R Saving _problems/test-ArmadilloOAuth-1051.R Saving _problems/test-ArmadilloOAuth-1064.R Saving _problems/test-ArmadilloOAuth-1079.R Saving _problems/test-ArmadilloOAuth-1091.R Saving _problems/test-ArmadilloOAuth-1112.R Saving _problems/test-ArmadilloOAuth-1126.R Saving _problems/test-ArmadilloOAuth-1151.R Saving _problems/test-ArmadilloResult-23.R Saving _problems/test-ArmadilloResult-53.R Saving _problems/test-ArmadilloResult-79.R Saving _problems/test-ArmadilloResult-102.R Saving _problems/test-ArmadilloResult-125.R Saving _problems/test-ArmadilloResult-157.R Saving _problems/test-utils-19.R Saving _problems/test-utils-23.R Saving _problems/test-utils-23.R Saving _problems/test-utils-39.R Saving _problems/test-utils-63.R Saving _problems/test-utils-75.R Saving _problems/test-utils-115.R Saving _problems/test-utils-120.R Saving _problems/test-utils-120.R Saving _problems/test-utils-145.R Saving _problems/test-utils-150.R Saving _problems/test-utils-150.R [ FAIL 72 | WARN 0 | SKIP 1 | PASS 45 ] ══ Skipped tests (1) ═══════════════════════════════════════════════════════════ • I still don't get the .deparse arguments (1): 'test-utils.R:91:3' ══ Failed tests ════════════════════════════════════════════════════════════════ ── Error ('test-ArmadilloConnection.R:3:3'): dsDisconnect calls /logout endpoint ── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test-ArmadilloConnection.R:3:3 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) ── Error ('test-ArmadilloConnection.R:17:3'): dsDisconnect saves the workspace ── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test-ArmadilloConnection.R:17:3 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) ── Error ('test-ArmadilloConnection.R:35:3'): dsListProfiles retrieves profiles ── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test-ArmadilloConnection.R:35:3 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) ── Error ('test-ArmadilloConnection.R:52:3'): dsListProfiles returns default result if none found ── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test-ArmadilloConnection.R:52:3 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) ── Error ('test-ArmadilloConnection.R:72:3'): dsListTables retrieves tables ──── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test-ArmadilloConnection.R:72:3 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) ── Error ('test-ArmadilloConnection.R:89:3'): dsListResources retrieves resources ── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test-ArmadilloConnection.R:89:3 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) ── Error ('test-ArmadilloConnection.R:104:3'): dsHasTable returns TRUE if table exists ── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test-ArmadilloConnection.R:104:3 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) ── Error ('test-ArmadilloConnection.R:118:3'): dsHasTable returns FALSE if table doesnot exist ── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test-ArmadilloConnection.R:118:3 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) ── Error ('test-ArmadilloConnection.R:132:3'): dsHasResource returns TRUE if resource exists ── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test-ArmadilloConnection.R:132:3 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) ── Error ('test-ArmadilloConnection.R:146:3'): dsHasResource returns FALSE if table doesnot exist ── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test-ArmadilloConnection.R:146:3 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) ── Error ('test-ArmadilloConnection.R:174:3'): dsListSymbols returns symbols ─── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test-ArmadilloConnection.R:174:3 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) ── Error ('test-ArmadilloConnection.R:189:3'): dsRmSymbol removes symbol ─────── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test-ArmadilloConnection.R:189:3 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) ── Error ('test-ArmadilloConnection.R:202:3'): dsAssignTable assigns table to symbol ── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test-ArmadilloConnection.R:202:3 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) ── Error ('test-ArmadilloConnection.R:222:3'): dsAssignTable allows variable selection ── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test-ArmadilloConnection.R:222:3 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) ── Error ('test-ArmadilloConnection.R:248:3'): dsAssignTable, when called synchronously, waits for result ── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test-ArmadilloConnection.R:248:3 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) ── Error ('test-ArmadilloConnection.R:269:3'): dsAssignResource assigns resource to symbol ── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test-ArmadilloConnection.R:269:3 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) ── Error ('test-ArmadilloConnection.R:291:3'): dsAssignResource, when called synchronously, waits for result ── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test-ArmadilloConnection.R:291:3 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) ── Error ('test-ArmadilloConnection.R:325:3'): dsListMethods returns assign methods ── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test-ArmadilloConnection.R:325:3 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) ── Error ('test-ArmadilloConnection.R:364:3'): dsListPackages extracts name and version from packages ── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test-ArmadilloConnection.R:364:3 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) ── Error ('test-ArmadilloConnection.R:395:3'): dsListWorkspaces lists workspaces ── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test-ArmadilloConnection.R:395:3 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) ── Error ('test-ArmadilloConnection.R:420:3'): dsSaveWorkspace saves workspace ── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test-ArmadilloConnection.R:420:3 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) ── Error ('test-ArmadilloConnection.R:436:3'): dsRmWorkspace removes workspace ── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test-ArmadilloConnection.R:436:3 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) ── Error ('test-ArmadilloConnection.R:451:3'): dsAssignExpr assigns expression to symbol ── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test-ArmadilloConnection.R:451:3 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) ── Error ('test-ArmadilloConnection.R:470:3'): dsAssignExpr deparses function calls in expression ── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test-ArmadilloConnection.R:470:3 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) ── Error ('test-ArmadilloConnection.R:489:3'): dsAssignExpr, when called synchronously, waits for result ── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test-ArmadilloConnection.R:489:3 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) ── Error ('test-ArmadilloConnection.R:517:3'): dsAggregate executes deparsed query ── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test-ArmadilloConnection.R:517:3 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) ── Error ('test-ArmadilloConnection.R:538:3'): dsAssignExpr, when called synchronously, waits for result ── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test-ArmadilloConnection.R:538:3 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) ── Failure ('test-ArmadilloConnection.R:572:3'): dsAssignExpr handles error when called synchronously ── `with_mock(...)` threw an error with unexpected message. Expected match: "Internal server error: Error" Actual message: "`with_mock()` was deprecated in testthat 3.2.0 and is now defunct.\nℹ Please use `with_mocked_bindings()` instead." Backtrace: ▆ 1. ├─testthat::expect_error(...) at test-ArmadilloConnection.R:572:3 2. │ └─testthat:::quasi_capture(...) 3. │ ├─testthat (local) .capture(...) 4. │ │ └─base::withCallingHandlers(...) 5. │ └─rlang::eval_bare(quo_get_expr(.quo), quo_get_env(.quo)) 6. └─testthat::with_mock(...) 7. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 8. └─lifecycle:::deprecate_stop0(msg) 9. └─rlang::cnd_signal(...) ── Error ('test-ArmadilloConnection.R:601:3'): dsGetInfo returns server info ─── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test-ArmadilloConnection.R:601:3 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) ── Error ('test-ArmadilloConnection.R:625:3'): dsKeepAlive pings server info endpoint ── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test-ArmadilloConnection.R:625:3 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) ── Error ('test-ArmadilloDriver.R:19:3'): dsConnect returns an ArmadilloConnection ── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test-ArmadilloDriver.R:19:3 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) ── Error ('test-ArmadilloDriver.R:49:3'): dsConnect selects profile if one is provided ── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test-ArmadilloDriver.R:49:3 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) ── Error ('test-ArmadilloDriver.R:80:3'): dsConnect returns an ArmadilloConnection ── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test-ArmadilloDriver.R:80:3 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) ── Error ('test-ArmadilloDriver.R:110:3'): dsConnect can log in with bearer token ── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test-ArmadilloDriver.R:110:3 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) ── Error ('test-ArmadilloDriver.R:139:3'): dsConnect restores user workspace ─── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test-ArmadilloDriver.R:139:3 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) ── Error ('test-ArmadilloOAuth.R:11:3'): get_token returns the id_token from credentials ── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test-ArmadilloOAuth.R:11:3 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) ── Error ('test-ArmadilloOAuth.R:41:3'): armadillo.get_credentials returns a valid ArmadilloCredentials object ── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test-ArmadilloOAuth.R:41:3 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) ── Error ('test-ArmadilloOAuth.R:86:3'): .refresh_token returns success message if new credentials are not null and option fusionauth ── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test-ArmadilloOAuth.R:86:3 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) ── Error ('test-ArmadilloOAuth.R:146:3'): .refresh_token returns success message if new credentials are not null and option keycloak ── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test-ArmadilloOAuth.R:146:3 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) ── Error ('test-ArmadilloOAuth.R:191:3'): .refresh_token stops with message if fieldErrors returned with fusionauth flow ── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test-ArmadilloOAuth.R:191:3 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) ── Error ('test-ArmadilloOAuth.R:220:3'): .refresh_token stops with generic message if refresh fails silently fusionauth flow ── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test-ArmadilloOAuth.R:220:3 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) ── Error ('test-ArmadilloOAuth.R:261:3'): .refresh_token stops with message if fieldErrors returned with keycloak flow ── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test-ArmadilloOAuth.R:261:3 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) ── Error ('test-ArmadilloOAuth.R:290:3'): .refresh_token stops with generic message if refresh fails silently keycloak flow ── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test-ArmadilloOAuth.R:290:3 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) ── Error ('test-ArmadilloOAuth.R:308:3'): .get_oauth_info returns content when request is successful ── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test-ArmadilloOAuth.R:308:3 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) ── Error ('test-ArmadilloOAuth.R:324:3'): .get_oauth_info stops if server info fetch fails ── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test-ArmadilloOAuth.R:324:3 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) ── Error ('test-ArmadilloOAuth.R:1030:3'): .get_updated_expiry_date returns correct expiry time ── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test-ArmadilloOAuth.R:1030:3 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) ── Error ('test-ArmadilloOAuth.R:1045:3'): .refresh_token_safely returns refreshed connection if successful ── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test-ArmadilloOAuth.R:1045:3 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) ── Error ('test-ArmadilloOAuth.R:1058:3'): .refresh_token_safely returns original connection if refresh returns non-connection ── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test-ArmadilloOAuth.R:1058:3 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) ── Error ('test-ArmadilloOAuth.R:1070:3'): .refresh_token_safely returns original connection and warns if error occurs ── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test-ArmadilloOAuth.R:1070:3 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) ── Error ('test-ArmadilloOAuth.R:1085:3'): .reset_token_if_expired returns NULL if credentials are NULL ── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test-ArmadilloOAuth.R:1085:3 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) ── Error ('test-ArmadilloOAuth.R:1106:3'): .reset_token_if_expired returns NULL if token has not expired ── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test-ArmadilloOAuth.R:1106:3 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) ── Error ('test-ArmadilloOAuth.R:1118:3'): .check_multiple_conns throws error when multiple connections are found ── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test-ArmadilloOAuth.R:1118:3 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) ── Error ('test-ArmadilloOAuth.R:1144:3'): .getDSConnectionsMod returns flag = 2 when multiple DSConnection lists are found ── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test-ArmadilloOAuth.R:1144:3 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) ── Error ('test-ArmadilloResult.R:18:3'): getInfo awaits pending result and retrieves command info ── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test-ArmadilloResult.R:18:3 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) ── Error ('test-ArmadilloResult.R:49:3'): dsFetch retrieves last result for pending result ── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test-ArmadilloResult.R:49:3 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) ── Error ('test-ArmadilloResult.R:75:3'): dsIsCompleted retrieves status of COMPLETED async command ── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test-ArmadilloResult.R:75:3 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) ── Error ('test-ArmadilloResult.R:98:3'): dsIsCompleted retrieves status of FAILED async command ── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test-ArmadilloResult.R:98:3 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) ── Error ('test-ArmadilloResult.R:121:3'): dsIsCompleted retrieves status of RUNNING async command ── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test-ArmadilloResult.R:121:3 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) ── Error ('test-ArmadilloResult.R:153:3'): getInfo returns correct list for 404 errors ── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test-ArmadilloResult.R:153:3 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) ── Failure ('test-utils.R:12:3'): .handle_last_command_error throws error message ── `with_mock(...)` threw an error with unexpected message. Expected match: "Error" Actual message: "`with_mock()` was deprecated in testthat 3.2.0 and is now defunct.\nℹ Please use `with_mocked_bindings()` instead." Backtrace: ▆ 1. ├─testthat::expect_error(...) at test-utils.R:12:3 2. │ └─testthat:::quasi_capture(...) 3. │ ├─testthat (local) .capture(...) 4. │ │ └─base::withCallingHandlers(...) 5. │ └─rlang::eval_bare(quo_get_expr(.quo), quo_get_env(.quo)) 6. └─testthat::with_mock(...) 7. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 8. └─lifecycle:::deprecate_stop0(msg) 9. └─rlang::cnd_signal(...) ── Failure ('test-utils.R:20:3'): .handle_last_command_error throws error message ── arguments list number 1 not found in mock object ── Error ('test-utils.R:20:3'): .handle_last_command_error throws error message ── Error in `mock_args(mock_object)[[n]]`: subscript out of bounds Backtrace: ▆ 1. └─mockery::expect_args(...) at test-utils.R:20:3 ── Error ('test-utils.R:35:3'): .handle_last_command_error only works if status is FAILED ── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test-utils.R:35:3 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) ── Error ('test-utils.R:57:3'): .handle_request_error handles 400 ────────────── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test-utils.R:57:3 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) ── Error ('test-utils.R:69:3'): .handle_request_error handles 500 ────────────── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test-utils.R:69:3 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) ── Failure ('test-utils.R:107:3'): .retry_until_last_result handles 404 by retrieving lastcommand ── `with_mock(...)` threw an error with unexpected message. Expected match: "Command 'broken command' failed on test_cohort: Error whilst evaluating" Actual message: "`with_mock()` was deprecated in testthat 3.2.0 and is now defunct.\nℹ Please use `with_mocked_bindings()` instead." Backtrace: ▆ 1. ├─testthat::expect_error(...) at test-utils.R:107:3 2. │ └─testthat:::quasi_capture(...) 3. │ ├─testthat (local) .capture(...) 4. │ │ └─base::withCallingHandlers(...) 5. │ └─rlang::eval_bare(quo_get_expr(.quo), quo_get_env(.quo)) 6. └─testthat::with_mock(...) 7. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 8. └─lifecycle:::deprecate_stop0(msg) 9. └─rlang::cnd_signal(...) ── Failure ('test-utils.R:117:3'): .retry_until_last_result handles 404 by retrieving lastcommand ── arguments list number 1 not found in mock object ── Error ('test-utils.R:117:3'): .retry_until_last_result handles 404 by retrieving lastcommand ── Error in `mock_args(mock_object)[[n]]`: subscript out of bounds Backtrace: ▆ 1. └─mockery::expect_args(...) at test-utils.R:117:3 ── Failure ('test-utils.R:137:3'): .retry_until_last_result handles 404 by retrieving lastcommand ── `with_mock(...)` threw an error with unexpected message. Expected match: "Command 'broken command' failed on test_cohort: Error whilst evaluating" Actual message: "`with_mock()` was deprecated in testthat 3.2.0 and is now defunct.\nℹ Please use `with_mocked_bindings()` instead." Backtrace: ▆ 1. ├─testthat::expect_error(...) at test-utils.R:137:3 2. │ └─testthat:::quasi_capture(...) 3. │ ├─testthat (local) .capture(...) 4. │ │ └─base::withCallingHandlers(...) 5. │ └─rlang::eval_bare(quo_get_expr(.quo), quo_get_env(.quo)) 6. └─testthat::with_mock(...) 7. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 8. └─lifecycle:::deprecate_stop0(msg) 9. └─rlang::cnd_signal(...) ── Failure ('test-utils.R:147:3'): .retry_until_last_result handles 404 by retrieving lastcommand ── arguments list number 1 not found in mock object ── Error ('test-utils.R:147:3'): .retry_until_last_result handles 404 by retrieving lastcommand ── Error in `mock_args(mock_object)[[n]]`: subscript out of bounds Backtrace: ▆ 1. └─mockery::expect_args(...) at test-utils.R:147:3 ── Error ('test-utils.R:164:3'): .handle_last_command_error handles 404 error ── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. ├─testthat::expect_silent(...) at test-utils.R:164:3 2. │ └─testthat:::quasi_capture(enquo(object), NULL, evaluate_promise) 3. │ ├─testthat (local) .capture(...) 4. │ │ ├─withr::with_output_sink(...) 5. │ │ │ └─base::force(code) 6. │ │ ├─base::withCallingHandlers(...) 7. │ │ └─base::withVisible(code) 8. │ └─rlang::eval_bare(quo_get_expr(.quo), quo_get_env(.quo)) 9. └─testthat::with_mock(...) 10. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 11. └─lifecycle:::deprecate_stop0(msg) 12. └─rlang::cnd_signal(...) [ FAIL 72 | WARN 0 | SKIP 1 | PASS 45 ] Error: ! Test failures. Execution halted Package: EDISON Check: tests New result: ERROR Running ‘test-all.R’ [5s/5s] Running the tests in ‘tests/test-all.R’ failed. Complete output: > library(testthat) > test_check("EDISON") Loading required package: EDISON Loading required package: corpcor Loading required package: MASS [1] "Initialisation successful." [1] "Starting tvDBN iterations..." 5 % 10 % 15 % 20 % 25 % 30 % 35 % 40 % 45 % 50 % 55 % 60 % 65 % 70 % 75 % 80 % 85 % 90 % 95 % 100 % [1] "" [1] "End of iterations" [1] "Initialisation successful." [1] "Starting tvDBN iterations..." 5 % 10 % 15 % 20 % 25 % 30 % 35 % 40 % 45 % 50 % 55 % 60 % 65 % 70 % 75 % 80 % 85 % 90 % 95 % 100 % [1] "" [1] "End of iterations" Saving _problems/test-structure-moves-329.R Saving _problems/test-structure-moves-333.R Saving _problems/test-structure-moves-337.R [ FAIL 3 | WARN 0 | SKIP 0 | PASS 17 ] ══ Failed tests ════════════════════════════════════════════════════════════════ ── Error ('test-structure-moves.r:328:1'): network info the same before and after ── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(updateCorrectly(), is_true()) ── Error ('test-structure-moves.r:332:1'): rejected moves make no change ─────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(noChangeOnReject(), is_true()) ── Error ('test-structure-moves.r:336:1'): output not null ───────────────────── Error in `is_false()`: could not find function "is_false" Backtrace: ▆ 1. └─testthat::expect_that(...) [ FAIL 3 | WARN 0 | SKIP 0 | PASS 17 ] Error: ! Test failures. Execution halted Package: ergm Check: tests New result: ERROR Running ‘requireNamespaceTest.R’ [3s/3s] Running ‘testthat.R’ [325s/173s] Running the tests in ‘tests/testthat.R’ failed. Complete output: > # File tests/testthat.R in package ergm, part of the Statnet suite of packages > # for network analysis, https://statnet.org . > # > # This software is distributed under the GPL-3 license. It is free, open > # source, and has the attribution requirements (GPL Section 7) at > # https://statnet.org/attribution . > # > # Copyright 2003-2025 Statnet Commons > ################################################################################ > library(testthat) > library(ergm) Loading required package: network 'network' 1.19.0 (2024-12-08), part of the Statnet Project * 'news(package="network")' for changes since last version * 'citation("network")' for citation information * 'https://statnet.org' for help, support, and other information 'ergm' 4.10.1 (2025-08-26), part of the Statnet Project * 'news(package="ergm")' for changes since last version * 'citation("ergm")' for citation information * 'https://statnet.org' for help, support, and other information 'ergm' 4 is a major update that introduces some backwards-incompatible changes. Please type 'news(package="ergm")' for a list of major changes. > > test_check("ergm") Starting 2 test processes. > test-basis.R: Starting maximum pseudolikelihood estimation (MPLE): > test-basis.R: Obtaining the responsible dyads. > test-basis.R: Evaluating the predictor and response matrix. > test-basis.R: Maximizing the pseudolikelihood. > test-basis.R: Finished MPLE. > test-basis.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-basis.R: Iteration 1 of at most 60: > test-basis.R: 1 > test-basis.R: Optimizing with step length 1.0000. > test-basis.R: The log-likelihood improved by 0.6124. > test-basis.R: Estimating equations are not within tolerance region. > test-basis.R: Iteration 2 of at most 60: > test-bd.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'. > test-basis.R: 1 > test-basis.R: Optimizing with step length 1.0000. > test-basis.R: The log-likelihood improved by 0.0128. > test-basis.R: Convergence test p-value: 0.0011. Converged with 99% confidence. > test-basis.R: Finished MCMLE. > test-basis.R: Evaluating log-likelihood at the estimate. > test-basis.R: Fitting the dyad-independent submodel... > test-basis.R: Bridging between the dyad-independent submodel and the full model... > test-basis.R: Setting up bridge sampling... > test-basis.R: Using 16 bridges: 1 > test-basis.R: 2 > test-basis.R: 3 > test-basis.R: 4 > test-basis.R: 5 > test-basis.R: 6 > test-basis.R: 7 > test-basis.R: 8 > test-basis.R: 9 > test-basis.R: 10 > test-basis.R: 11 > test-basis.R: 12 > test-basis.R: 13 > test-basis.R: 14 > test-basis.R: 15 > test-basis.R: 16 > test-basis.R: . > test-basis.R: Bridging finished. > test-basis.R: > test-basis.R: This model was fit using MCMC. To examine model diagnostics and check > test-basis.R: for degeneracy, use the mcmc.diagnostics() function. > test-basis.R: Starting maximum pseudolikelihood estimation (MPLE): > test-basis.R: Obtaining the responsible dyads. > test-basis.R: Evaluating the predictor and response matrix. > test-basis.R: Maximizing the pseudolikelihood. > test-basis.R: Finished MPLE. > test-basis.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-basis.R: Iteration 1 of at most 60: > test-basis.R: 1 > test-basis.R: Optimizing with step length 1.0000. > test-basis.R: The log-likelihood improved by 0.6124. > test-basis.R: Estimating equations are not within tolerance region. > test-basis.R: Iteration 2 of at most 60: > test-basis.R: 1 Optimizing with step length 1.0000. > test-basis.R: The log-likelihood improved by 0.0128. > test-basis.R: Convergence test p-value: 0.0011. Converged with 99% confidence. > test-basis.R: Finished MCMLE. > test-basis.R: Evaluating log-likelihood at the estimate. > test-basis.R: Fitting the dyad-independent submodel... > test-basis.R: Bridging between the dyad-independent submodel and the full model... > test-basis.R: Setting up bridge sampling... > test-basis.R: Using 16 bridges: > test-basis.R: 1 > test-basis.R: 2 > test-basis.R: 3 > test-basis.R: 4 > test-basis.R: 5 > test-basis.R: 6 > test-basis.R: 7 > test-basis.R: 8 > test-basis.R: 9 > test-basis.R: 10 > test-basis.R: 11 > test-basis.R: 12 13 > test-basis.R: 14 > test-basis.R: 15 > test-bd.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'. > test-basis.R: 16 > test-basis.R: . > test-basis.R: Bridging finished. > test-basis.R: > test-basis.R: This model was fit using MCMC. To examine model diagnostics and check > test-basis.R: for degeneracy, use the mcmc.diagnostics() function. > test-basis.R: Starting maximum pseudolikelihood estimation (MPLE): > test-basis.R: Obtaining the responsible dyads. > test-basis.R: Evaluating the predictor and response matrix. > test-basis.R: Maximizing the pseudolikelihood. > test-basis.R: Finished MPLE. > test-basis.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-basis.R: Iteration 1 of at most 60: > test-bd.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'. > test-basis.R: 1 > test-basis.R: Optimizing with step length 1.0000. > test-basis.R: The log-likelihood improved by 0.6124. > test-basis.R: Estimating equations are not within tolerance region. > test-basis.R: Iteration 2 of at most 60: > test-basis.R: 1 > test-basis.R: Optimizing with step length 1.0000. > test-basis.R: The log-likelihood improved by 0.0128. > test-basis.R: Convergence test p-value: 0.0011. Converged with 99% confidence. > test-basis.R: Finished MCMLE. > test-basis.R: Evaluating log-likelihood at the estimate. > test-basis.R: Fitting the dyad-independent submodel... > test-basis.R: Bridging between the dyad-independent submodel and the full model... > test-basis.R: Setting up bridge sampling... > test-basis.R: Using 16 bridges: 1 > test-basis.R: 2 > test-basis.R: 3 > test-basis.R: 4 > test-basis.R: 5 > test-basis.R: 6 > test-basis.R: 7 > test-basis.R: 8 > test-basis.R: 9 > test-basis.R: 10 > test-basis.R: 11 > test-basis.R: 12 > test-basis.R: 13 > test-basis.R: 14 > test-basis.R: 15 > test-basis.R: 16 > test-basis.R: . > test-basis.R: Bridging finished. > test-basis.R: > test-basis.R: This model was fit using MCMC. To examine model diagnostics and check > test-basis.R: for degeneracy, use the mcmc.diagnostics() function. > test-basis.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'. > test-basis.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'. > test-basis.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'. > test-basis.R: Model and/or observational constraints are not dyad-independent. Dyad imputation cannot be used. Please ensure your LHS network satisfies all constraints. > test-basis.R: Starting contrastive divergence estimation via CD-MCMLE: > test-basis.R: Iteration 1 of at most 60: > test-basis.R: Convergence test P-value:4.7e-173 > test-basis.R: 1 > test-basis.R: The log-likelihood improved by 1.861. > test-basis.R: Iteration 2 of at most 60: > test-basis.R: Convergence test P-value:2.5e-135 > test-basis.R: 1 > test-basis.R: The log-likelihood improved by 1.577. > test-basis.R: Iteration 3 of at most 60: > test-basis.R: Convergence test P-value:1.9e-80 > test-basis.R: 1 > test-basis.R: The log-likelihood improved by 0.8158. > test-basis.R: Iteration 4 of at most 60: > test-basis.R: Convergence test P-value:4.5e-32 > test-basis.R: 1 > test-basis.R: The log-likelihood improved by 0.2411. > test-basis.R: Iteration 5 of at most 60: > test-basis.R: Convergence test P-value:1.3e-09 > test-basis.R: 1 > test-basis.R: The log-likelihood improved by 0.0608. > test-basis.R: Iteration 6 of at most 60: > test-basis.R: Convergence test P-value:2.8e-03 > test-basis.R: 1 > test-basis.R: The log-likelihood improved by 0.01871. > test-basis.R: Iteration 7 of at most 60: > test-basis.R: Convergence test P-value:7.5e-01 > test-basis.R: Convergence detected. Stopping. > test-basis.R: 1 > test-basis.R: The log-likelihood improved by 0.001578. > test-basis.R: Finished CD. > test-basis.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-basis.R: Iteration 1 of at most 60: > test-bridge-target.stats.R: > test-bridge-target.stats.R: Attaching package: 'statnet.common' > test-bridge-target.stats.R: > test-bridge-target.stats.R: The following objects are masked from 'package:base': > test-bridge-target.stats.R: > test-bridge-target.stats.R: attr, order, replace > test-bridge-target.stats.R: > test-bridge-target.stats.R: Starting maximum pseudolikelihood estimation (MPLE): > test-bridge-target.stats.R: Obtaining the responsible dyads. > test-bridge-target.stats.R: Evaluating the predictor and response matrix. > test-bridge-target.stats.R: Maximizing the pseudolikelihood. > test-bridge-target.stats.R: Finished MPLE. > test-bridge-target.stats.R: Evaluating log-likelihood at the estimate. > test-bridge-target.stats.R: > test-bridge-target.stats.R: Unable to match target stats. Using MCMLE estimation. > test-bridge-target.stats.R: Starting maximum pseudolikelihood estimation (MPLE): > test-bridge-target.stats.R: Obtaining the responsible dyads. > test-bridge-target.stats.R: Evaluating the predictor and response matrix. > test-bridge-target.stats.R: Maximizing the pseudolikelihood. > test-bridge-target.stats.R: Finished MPLE. > test-bridge-target.stats.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-bridge-target.stats.R: Iteration 1 of at most 60: > test-basis.R: 1 > test-basis.R: Optimizing with step length 1.0000. > test-basis.R: The log-likelihood improved by 0.1892. > test-basis.R: Estimating equations are not within tolerance region. > test-basis.R: Iteration 2 of at most 60: > test-bridge-target.stats.R: 1 > test-bridge-target.stats.R: Optimizing with step length 1.0000. > test-bridge-target.stats.R: The log-likelihood improved by 0.0219. > test-bridge-target.stats.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-bridge-target.stats.R: Finished MCMLE. > test-bridge-target.stats.R: Evaluating log-likelihood at the estimate. > test-bridge-target.stats.R: Fitting the dyad-independent submodel... > test-bridge-target.stats.R: Bridging between the dyad-independent submodel and the full model... > test-bridge-target.stats.R: Setting up bridge sampling... > test-basis.R: 1 > test-bridge-target.stats.R: Using 16 bridges: 1 > test-basis.R: Optimizing with step length 1.0000. > test-bridge-target.stats.R: 2 > test-bridge-target.stats.R: 3 > test-basis.R: The log-likelihood improved by 0.0072. > test-bridge-target.stats.R: 4 > test-bridge-target.stats.R: 5 > test-basis.R: Convergence test p-value: 0.0001. Converged with 99% confidence. > test-basis.R: Finished MCMLE. > test-bridge-target.stats.R: 6 > test-basis.R: Evaluating log-likelihood at the estimate. > test-basis.R: Setting up bridge sampling... > test-bridge-target.stats.R: 7 > test-bridge-target.stats.R: 8 > test-bridge-target.stats.R: 9 > test-basis.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'. > test-bridge-target.stats.R: 10 > test-bridge-target.stats.R: 11 > test-bridge-target.stats.R: 12 > test-basis.R: Using 16 bridges: 1 > test-bridge-target.stats.R: 13 > test-basis.R: 2 > test-basis.R: 3 > test-bridge-target.stats.R: 14 > test-basis.R: 4 > test-bridge-target.stats.R: 15 > test-bridge-target.stats.R: 16 > test-basis.R: 5 > test-bridge-target.stats.R: . > test-bridge-target.stats.R: Bridging finished. > test-bridge-target.stats.R: > test-bridge-target.stats.R: This model was fit using MCMC. To examine model diagnostics and check > test-bridge-target.stats.R: for degeneracy, use the mcmc.diagnostics() function. > test-basis.R: 6 > test-basis.R: 7 > test-basis.R: 8 > test-bridge-target.stats.R: Fitting the dyad-independent submodel... > test-basis.R: 9 > test-basis.R: 10 > test-basis.R: 11 > test-basis.R: 12 > test-basis.R: 13 > test-basis.R: 14 > test-bridge-target.stats.R: Bridging between the dyad-independent submodel and the full model... > test-bridge-target.stats.R: Setting up bridge sampling... > test-basis.R: 15 > test-basis.R: 16 > test-basis.R: . > test-basis.R: Note: The constraint on the sample space is not dyad-independent. Null > test-basis.R: model likelihood is only implemented for dyad-independent constraints > test-basis.R: at this time. Number of observations is similarly poorly defined. This > test-basis.R: means that all likelihood-based inference (LRT, Analysis of Deviance, > test-basis.R: AIC, BIC, etc.) is only valid between models with the same reference > test-basis.R: distribution and constraints. > test-basis.R: > test-basis.R: This model was fit using MCMC. To examine model diagnostics and check > test-basis.R: for degeneracy, use the mcmc.diagnostics() function. > test-bridge-target.stats.R: Using 16 bridges: > test-bridge-target.stats.R: 1 > test-basis.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'. > test-basis.R: Model and/or observational constraints are not dyad-independent. Dyad imputation cannot be used. Please ensure your LHS network satisfies all constraints. > test-basis.R: Starting contrastive divergence estimation via CD-MCMLE: > test-basis.R: Iteration 1 of at most 60: > test-basis.R: Convergence test P-value:4.7e-173 > test-basis.R: 1 > test-basis.R: The log-likelihood improved by 1.861. > test-basis.R: Iteration 2 of at most 60: > test-basis.R: Convergence test P-value:2.5e-135 > test-basis.R: 1 > test-basis.R: The log-likelihood improved by 1.577. > test-basis.R: Iteration 3 of at most 60: > test-basis.R: Convergence test P-value:1.9e-80 > test-bridge-target.stats.R: 2 > test-basis.R: 1 > test-basis.R: The log-likelihood improved by 0.8158. > test-basis.R: Iteration 4 of at most 60: > test-basis.R: Convergence test P-value:4.5e-32 > test-basis.R: 1 > test-basis.R: The log-likelihood improved by 0.2411. > test-basis.R: Iteration 5 of at most 60: > test-basis.R: Convergence test P-value:1.3e-09 > test-basis.R: 1 > test-basis.R: The log-likelihood improved by 0.0608. > test-basis.R: Iteration 6 of at most 60: > test-basis.R: Convergence test P-value:2.8e-03 > test-basis.R: 1 > test-basis.R: The log-likelihood improved by 0.01871. > test-basis.R: Iteration 7 of at most 60: > test-basis.R: Convergence test P-value:7.5e-01 > test-basis.R: Convergence detected. Stopping. > test-basis.R: 1 > test-basis.R: The log-likelihood improved by 0.001578. > test-basis.R: Finished CD. > test-basis.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-basis.R: Iteration 1 of at most 60: > test-bridge-target.stats.R: 3 > test-bridge-target.stats.R: 4 > test-bridge-target.stats.R: 5 > test-bridge-target.stats.R: 6 > test-bridge-target.stats.R: 7 > test-bridge-target.stats.R: 8 > test-bridge-target.stats.R: 9 > test-bridge-target.stats.R: 10 > test-basis.R: 1 > test-basis.R: Optimizing with step length 1.0000. > test-basis.R: The log-likelihood improved by 0.1892. > test-basis.R: Estimating equations are not within tolerance region. > test-basis.R: Iteration 2 of at most 60: > test-bridge-target.stats.R: 11 > test-bridge-target.stats.R: 12 > test-bridge-target.stats.R: 13 > test-bridge-target.stats.R: 14 > test-basis.R: 1 > test-basis.R: Optimizing with step length 1.0000. > test-bridge-target.stats.R: 15 > test-basis.R: The log-likelihood improved by 0.0072. > test-bridge-target.stats.R: 16 > test-basis.R: Convergence test p-value: 0.0001. Converged with 99% confidence. > test-basis.R: Finished MCMLE. > test-basis.R: Evaluating log-likelihood at the estimate. > test-basis.R: Setting up bridge sampling... > test-bridge-target.stats.R: . > test-bridge-target.stats.R: Bridging finished. > test-bridge-target.stats.R: Fitting the dyad-independent submodel... > test-basis.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'. > test-basis.R: Using 16 bridges: > test-basis.R: 1 > test-basis.R: 2 > test-basis.R: 3 > test-basis.R: 4 > test-bridge-target.stats.R: Bridging between the dyad-independent submodel and the full model... > test-bridge-target.stats.R: Setting up bridge sampling... > test-basis.R: 5 > test-basis.R: 6 > test-basis.R: 7 > test-bridge-target.stats.R: Using 16 bridges: 1 > test-basis.R: 8 > test-basis.R: 9 > test-basis.R: 10 > test-basis.R: 11 > test-basis.R: 12 > test-basis.R: 13 > test-basis.R: 14 > test-basis.R: 15 > test-basis.R: 16 > test-basis.R: . > test-basis.R: Note: The constraint on the sample space is not dyad-independent. Null > test-basis.R: model likelihood is only implemented for dyad-independent constraints > test-basis.R: at this time. Number of observations is similarly poorly defined. This > test-basis.R: means that all likelihood-based inference (LRT, Analysis of Deviance, > test-basis.R: AIC, BIC, etc.) is only valid between models with the same reference > test-basis.R: distribution and constraints. > test-basis.R: > test-basis.R: This model was fit using MCMC. To examine model diagnostics and check > test-basis.R: for degeneracy, use the mcmc.diagnostics() function. > test-bridge-target.stats.R: 2 > test-basis.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'. > test-basis.R: Model and/or observational constraints are not dyad-independent. Dyad imputation cannot be used. Please ensure your LHS network > test-basis.R: satisfies all constraints. > test-basis.R: Starting contrastive divergence estimation via CD-MCMLE: > test-basis.R: Iteration 1 of at most 60: > test-basis.R: Convergence test P-value:4.7e-173 > test-basis.R: 1 > test-basis.R: The log-likelihood improved by 1.861. > test-basis.R: Iteration 2 of at most 60: > test-bridge-target.stats.R: 3 > test-basis.R: Convergence test P-value:2.5e-135 > test-basis.R: 1 > test-basis.R: The log-likelihood improved by 1.577. > test-basis.R: Iteration 3 of at most 60: > test-basis.R: Convergence test P-value:1.9e-80 > test-basis.R: 1 > test-basis.R: The log-likelihood improved by 0.8158. > test-basis.R: Iteration 4 of at most 60: > test-basis.R: Convergence test P-value:4.5e-32 > test-basis.R: 1 > test-basis.R: The log-likelihood improved by 0.2411. > test-basis.R: Iteration 5 of at most 60: > test-basis.R: Convergence test P-value:1.3e-09 > test-basis.R: 1 > test-basis.R: The log-likelihood improved by 0.0608. > test-basis.R: Iteration 6 of at most 60: > test-basis.R: Convergence test P-value:2.8e-03 > test-basis.R: 1 > test-basis.R: The log-likelihood improved by 0.01871. > test-basis.R: Iteration 7 of at most 60: > test-basis.R: Convergence test P-value:7.5e-01 > test-basis.R: Convergence detected. Stopping. > test-basis.R: 1 > test-bridge-target.stats.R: 4 > test-basis.R: The log-likelihood improved by 0.001578. > test-basis.R: Finished CD. > test-basis.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-basis.R: Iteration 1 of at most 60: > test-bridge-target.stats.R: 5 > test-bridge-target.stats.R: 6 > test-bridge-target.stats.R: 7 > test-bridge-target.stats.R: 8 > test-bridge-target.stats.R: 9 > test-basis.R: 1 > test-basis.R: Optimizing with step length 1.0000. > test-basis.R: The log-likelihood improved by 0.1892. > test-basis.R: Estimating equations are not within tolerance region. > test-basis.R: Iteration 2 of at most 60: > test-bridge-target.stats.R: 10 > test-bridge-target.stats.R: 11 > test-bridge-target.stats.R: 12 > test-basis.R: 1 > test-basis.R: Optimizing with step length 1.0000. > test-basis.R: The log-likelihood improved by 0.0072. > test-basis.R: Convergence test p-value: 0.0001. Converged with 99% confidence. > test-basis.R: Finished MCMLE. > test-basis.R: Evaluating log-likelihood at the estimate. > test-basis.R: Setting up bridge sampling... > test-bridge-target.stats.R: 13 > test-basis.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'. > test-basis.R: Using 16 bridges: 1 > test-basis.R: 2 > test-basis.R: 3 > test-basis.R: 4 > test-basis.R: 5 > test-bridge-target.stats.R: 14 > test-basis.R: 6 > test-basis.R: 7 > test-basis.R: 8 > test-basis.R: 9 > test-basis.R: 10 > test-basis.R: 11 > test-basis.R: 12 > test-basis.R: 13 > test-basis.R: 14 > test-bridge-target.stats.R: 15 > test-basis.R: 15 > test-basis.R: 16 > test-basis.R: . > test-basis.R: Note: The constraint on the sample space is not dyad-independent. Null > test-basis.R: model likelihood is only implemented for dyad-independent constraints > test-basis.R: at this time. Number of observations is similarly poorly defined. This > test-basis.R: means that all likelihood-based inference (LRT, Analysis of Deviance, > test-basis.R: AIC, BIC, etc.) is only valid between models with the same reference > test-basis.R: distribution and constraints. > test-basis.R: > test-basis.R: This model was fit using MCMC. To examine model diagnostics and check > test-basis.R: for degeneracy, use the mcmc.diagnostics() function. > test-bridge-target.stats.R: 16 > test-bridge-target.stats.R: . > test-bridge-target.stats.R: Bridging finished. > test-bridge-target.stats.R: Fitting the dyad-independent submodel... > test-bridge-target.stats.R: Bridging between the dyad-independent submodel and the full model... > test-bridge-target.stats.R: Setting up bridge sampling... > test-bridge-target.stats.R: Using 16 bridges: > test-bridge-target.stats.R: 1 > test-bridge-target.stats.R: 2 > test-bridge-target.stats.R: 3 > test-checkpointing.R: Starting maximum pseudolikelihood estimation (MPLE): > test-checkpointing.R: Obtaining the responsible dyads. > test-checkpointing.R: Evaluating the predictor and response matrix. > test-checkpointing.R: Maximizing the pseudolikelihood. > test-checkpointing.R: Finished MPLE. > test-checkpointing.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-checkpointing.R: Iteration 1 of at most 60: > test-checkpointing.R: Saving state in '/home/hornik/tmp/scratch/RtmpppdTEt/fileb88c13bc252d7_001.RData'. > test-bridge-target.stats.R: 4 > test-bridge-target.stats.R: 5 > test-bridge-target.stats.R: 6 > test-bridge-target.stats.R: 7 > test-bridge-target.stats.R: 8 > test-bridge-target.stats.R: 9 > test-checkpointing.R: 1 > test-checkpointing.R: Optimizing with step length 1.0000. > test-checkpointing.R: The log-likelihood improved by 0.0213. > test-checkpointing.R: Step length converged once. Increasing MCMC sample size. > test-checkpointing.R: Iteration 2 of at most 60: > test-checkpointing.R: Saving state in '/home/hornik/tmp/scratch/RtmpppdTEt/fileb88c13bc252d7_002.RData'. > test-bridge-target.stats.R: 10 > test-bridge-target.stats.R: 11 > test-bridge-target.stats.R: 12 > test-bridge-target.stats.R: 13 > test-checkpointing.R: 1 > test-checkpointing.R: Optimizing with step length 1.0000. > test-bridge-target.stats.R: 14 > test-checkpointing.R: The log-likelihood improved by 0.0238. > test-checkpointing.R: Step length converged twice. Stopping. > test-checkpointing.R: Finished MCMLE. > test-checkpointing.R: Evaluating log-likelihood at the estimate. > test-bridge-target.stats.R: 15 > test-checkpointing.R: Fitting the dyad-independent submodel... > test-bridge-target.stats.R: 16 > test-checkpointing.R: Bridging between the dyad-independent submodel and the full model... > test-checkpointing.R: Setting up bridge sampling... > test-bridge-target.stats.R: . > test-bridge-target.stats.R: Bridging finished. > test-bridge-target.stats.R: Fitting the dyad-independent submodel... > test-checkpointing.R: Using 16 bridges: 1 > test-checkpointing.R: 2 > test-checkpointing.R: 3 > test-bridge-target.stats.R: Bridging between the dyad-independent submodel and the full model... > test-bridge-target.stats.R: Setting up bridge sampling... > test-checkpointing.R: 4 > test-checkpointing.R: 5 > test-bridge-target.stats.R: Using 16 bridges: 1 > test-checkpointing.R: 6 > test-checkpointing.R: 7 > test-checkpointing.R: 8 > test-checkpointing.R: 9 > test-checkpointing.R: 10 > test-checkpointing.R: 11 > test-bridge-target.stats.R: 2 > test-checkpointing.R: 12 > test-checkpointing.R: 13 > test-checkpointing.R: 14 > test-checkpointing.R: 15 > test-checkpointing.R: 16 > test-bridge-target.stats.R: 3 > test-checkpointing.R: . > test-checkpointing.R: Bridging finished. > test-checkpointing.R: > test-checkpointing.R: This model was fit using MCMC. To examine model diagnostics and check > test-checkpointing.R: for degeneracy, use the mcmc.diagnostics() function. > test-checkpointing.R: Starting maximum pseudolikelihood estimation (MPLE): > test-checkpointing.R: Obtaining the responsible dyads. > test-checkpointing.R: Evaluating the predictor and response matrix. > test-checkpointing.R: Maximizing the pseudolikelihood. > test-bridge-target.stats.R: 4 > test-checkpointing.R: Finished MPLE. > test-checkpointing.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-checkpointing.R: Resuming from state saved in '/home/hornik/tmp/scratch/RtmpppdTEt/fileb88c13bc252d7_002.RData'. > test-checkpointing.R: Iteration 1 of at most 60: > test-bridge-target.stats.R: 5 > test-bridge-target.stats.R: 6 > test-bridge-target.stats.R: 7 > test-bridge-target.stats.R: 8 > test-checkpointing.R: 1 > test-checkpointing.R: Optimizing with step length 1.0000. > test-bridge-target.stats.R: 9 > test-checkpointing.R: The log-likelihood improved by 0.0145. > test-checkpointing.R: Step length converged twice. Stopping. > test-checkpointing.R: Finished MCMLE. > test-checkpointing.R: Evaluating log-likelihood at the estimate. > test-checkpointing.R: Fitting the dyad-independent submodel... > test-bridge-target.stats.R: 10 > test-checkpointing.R: Bridging between the dyad-independent submodel and the full model... > test-checkpointing.R: Setting up bridge sampling... > test-checkpointing.R: Using 16 bridges: 1 > test-bridge-target.stats.R: 11 > test-checkpointing.R: 2 > test-checkpointing.R: 3 > test-checkpointing.R: 4 > test-checkpointing.R: 5 > test-checkpointing.R: 6 > test-bridge-target.stats.R: 12 > test-checkpointing.R: 7 > test-checkpointing.R: 8 > test-checkpointing.R: 9 > test-checkpointing.R: 10 > test-checkpointing.R: 11 > test-checkpointing.R: 12 > test-bridge-target.stats.R: 13 > test-checkpointing.R: 13 > test-checkpointing.R: 14 > test-checkpointing.R: 15 > test-checkpointing.R: 16 > test-checkpointing.R: . > test-checkpointing.R: Bridging finished. > test-checkpointing.R: > test-checkpointing.R: This model was fit using MCMC. To examine model diagnostics and check > test-checkpointing.R: for degeneracy, use the mcmc.diagnostics() function. > test-bridge-target.stats.R: 14 > test-bridge-target.stats.R: 15 > test-bridge-target.stats.R: 16 > test-bridge-target.stats.R: . > test-bridge-target.stats.R: Bridging finished. > test-constrain-degrees-edges.R: Best valid proposal 'CondOutDegree' cannot take into account hint(s) 'triadic'. > test-constrain-degrees-edges.R: Model and/or observational constraints are not dyad-independent. Dyad imputation cannot be used. Please ensure your LHS network > test-constrain-degrees-edges.R: satisfies all constraints. > test-constrain-degrees-edges.R: Starting contrastive divergence estimation via CD-MCMLE: > test-constrain-degrees-edges.R: Iteration 1 of at most 2: > test-constrain-degrees-edges.R: Convergence test P-value:5.2e-06 > test-constrain-degrees-edges.R: 1 > test-constrain-degrees-edges.R: The log-likelihood improved by 0.05768. > test-constrain-degrees-edges.R: Iteration 2 of at most 2: > test-constrain-degrees-edges.R: Convergence test P-value:3.7e-03 > test-constrain-degrees-edges.R: 1 > test-constrain-degrees-edges.R: The log-likelihood improved by 0.09813. > test-constrain-degrees-edges.R: Finished CD. > test-constrain-degrees-edges.R: This model was fit using MCMC. To examine model diagnostics and check > test-constrain-degrees-edges.R: for degeneracy, use the mcmc.diagnostics() function. > test-constrain-degrees-edges.R: Best valid proposal 'CondOutDegree' cannot take into account hint(s) 'triadic'. > test-constrain-degrees-edges.R: Best valid proposal 'CondInDegree' cannot take into account hint(s) 'triadic'. > test-constrain-degrees-edges.R: Best valid proposal 'CondDegree' cannot take into account hint(s) 'triadic'. > test-constrain-degrees-edges.R: Best valid proposal 'CondDegree' cannot take into account hint(s) 'triadic'. > test-constrain-degrees-edges.R: Best valid proposal 'ConstantEdges' cannot take into account hint(s) 'triadic'. > test-constrain-blockdiag.R: Best valid proposal 'DistRLE' cannot take into account hint(s) 'sparse' and 'triadic'. > test-constrain-blockdiag.R: Best valid proposal 'DistRLE' cannot take into account hint(s) 'sparse' and 'triadic'. > test-constrain-blockdiag.R: > test-constrain-blockdiag.R: 'ergm.count' 4.1.3 (2025-09-10), part of the Statnet Project > test-constrain-blockdiag.R: * 'news(package="ergm.count")' for changes since last version > test-constrain-blockdiag.R: * 'citation("ergm.count")' for citation information > test-constrain-blockdiag.R: * 'https://statnet.org' for help, support, and other information > test-constrain-blockdiag.R: > test-constrain-degrees-edges.R: Best valid proposal 'ConstantEdges' cannot take into account hint(s) 'triadic'. > test-constrain-degrees-edges.R: Best valid proposal 'CondDegree' cannot take into account hint(s) 'triadic'. > test-constrain-degrees-edges.R: Best valid proposal 'ConstantEdges' cannot take into account hint(s) 'triadic'. > test-constrain-degrees-edges.R: Best valid proposal 'ConstantEdges' cannot take into account hint(s) 'triadic'. > test-constrain-dind.R: Starting maximum pseudolikelihood estimation (MPLE): > test-constrain-dind.R: Obtaining the responsible dyads. > test-constrain-dind.R: Evaluating the predictor and response matrix. > test-constrain-dind.R: Maximizing the pseudolikelihood. > test-constrain-dind.R: Finished MPLE. > test-constrain-dind.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-constrain-dind.R: Iteration 1 of at most 60: > test-constrain-dind.R: 1 > test-constrain-dind.R: Optimizing with step length 1.0000. > test-constrain-dind.R: The log-likelihood improved by 0.0020. > test-constrain-dind.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-constrain-dind.R: Finished MCMLE. > test-constrain-dind.R: Evaluating log-likelihood at the estimate. > test-constrain-dind.R: Fitting the dyad-independent submodel... > test-constrain-dind.R: Bridging between the dyad-independent submodel and the full model... > test-constrain-dind.R: Setting up bridge sampling... > test-constrain-dind.R: Using 16 bridges: 1 > test-constrain-dind.R: 2 > test-constrain-dind.R: 3 > test-constrain-dind.R: 4 > test-constrain-dind.R: 5 > test-constrain-dind.R: 6 > test-constrain-dind.R: 7 > test-constrain-dind.R: 8 > test-constrain-dind.R: 9 > test-constrain-dind.R: 10 > test-constrain-dind.R: 11 > test-constrain-dind.R: 12 > test-constrain-dind.R: 13 > test-constrain-dind.R: 14 > test-constrain-dind.R: 15 > test-constrain-dind.R: 16 > test-constrain-dind.R: . > test-constrain-dind.R: Bridging finished. > test-constrain-dind.R: > test-constrain-dind.R: This model was fit using MCMC. To examine model diagnostics and check > test-constrain-dind.R: for degeneracy, use the mcmc.diagnostics() function. > test-constrain-dind.R: Starting maximum pseudolikelihood estimation (MPLE): > test-constrain-dind.R: Obtaining the responsible dyads. > test-constrain-dind.R: Evaluating the predictor and response matrix. > test-constrain-dind.R: Maximizing the pseudolikelihood. > test-constrain-dind.R: Finished MPLE. > test-constrain-dind.R: Evaluating log-likelihood at the estimate. > test-constrain-dind.R: > test-constrain-dind.R: Starting maximum pseudolikelihood estimation (MPLE): > test-constrain-dind.R: Obtaining the responsible dyads. > test-constrain-dind.R: Evaluating the predictor and response matrix. > test-constrain-dind.R: Maximizing the pseudolikelihood. > test-constrain-dind.R: Finished MPLE. > test-constrain-dind.R: Evaluating log-likelihood at the estimate. > test-constrain-dind.R: Starting maximum pseudolikelihood estimation (MPLE): > test-constrain-dind.R: Obtaining the responsible dyads. > test-constrain-dind.R: Evaluating the predictor and response matrix. > test-constrain-dind.R: Maximizing the pseudolikelihood. > test-constrain-dind.R: Finished MPLE. > test-constrain-dind.R: Evaluating log-likelihood at the estimate. > test-constraints.R: Starting maximum pseudolikelihood estimation (MPLE): > test-constraints.R: Obtaining the responsible dyads. > test-constraints.R: Evaluating the predictor and response matrix. > test-constraints.R: Maximizing the pseudolikelihood. > test-constraints.R: Finished MPLE. > test-constraints.R: Evaluating log-likelihood at the estimate. > test-constraints.R: > test-constrain-dind.R: Starting maximum pseudolikelihood estimation (MPLE): > test-constrain-dind.R: Obtaining the responsible dyads. > test-constrain-dind.R: Evaluating the predictor and response matrix. > test-constrain-dind.R: Maximizing the pseudolikelihood. > test-constrain-dind.R: Finished MPLE. > test-constrain-dind.R: Evaluating log-likelihood at the estimate. > test-constrain-dind.R: > test-constrain-dind.R: Starting maximum pseudolikelihood estimation (MPLE): > test-constrain-dind.R: Obtaining the responsible dyads. > test-constrain-dind.R: Evaluating the predictor and response matrix. > test-constrain-dind.R: Maximizing the pseudolikelihood. > test-constrain-dind.R: Finished MPLE. > test-constrain-dind.R: Evaluating log-likelihood at the estimate. > test-constrain-dind.R: > test-constrain-dind.R: Starting maximum pseudolikelihood estimation (MPLE): > test-constrain-dind.R: Obtaining the responsible dyads. > test-constrain-dind.R: Evaluating the predictor and response matrix. > test-constrain-dind.R: Maximizing the pseudolikelihood. > test-constrain-dind.R: Finished MPLE. > test-constrain-dind.R: Evaluating log-likelihood at the estimate. > test-constraints.R: Starting maximum pseudolikelihood estimation (MPLE): > test-constraints.R: Obtaining the responsible dyads. > test-constraints.R: Evaluating the predictor and response matrix. > test-constraints.R: Maximizing the pseudolikelihood. > test-constraints.R: Finished MPLE. > test-constraints.R: Evaluating log-likelihood at the estimate. > test-constraints.R: > test-constraints.R: Starting maximum pseudolikelihood estimation (MPLE): > test-constraints.R: Obtaining the responsible dyads. > test-constraints.R: Evaluating the predictor and response matrix. > test-constraints.R: Maximizing the pseudolikelihood. > test-constraints.R: Finished MPLE. > test-constraints.R: Evaluating log-likelihood at the estimate. > test-constraints.R: > test-drop.R: Observed statistic(s) edgecov.samplike.m - 1/2 are at their greatest attainable values. Their coefficients will be fixed at +Inf. > test-drop.R: All terms are either offsets or extreme values. No optimization is performed. > test-drop.R: Evaluating log-likelihood at the estimate. > test-drop.R: > test-drop.R: Observed statistic(s) edgecov.samplike.m - 1/2 are at their greatest attainable values. Their coefficients will be fixed at +Inf. > test-drop.R: All terms are either offsets or extreme values. No optimization is performed. > test-drop.R: Evaluating log-likelihood at the estimate. > test-drop.R: > test-drop.R: Observed statistic(s) edgecov.-samplike.m are at their smallest attainable values. Their coefficients will be fixed at -Inf. > test-drop.R: All terms are either offsets or extreme values. No optimization is performed. > test-drop.R: Evaluating log-likelihood at the estimate. > test-drop.R: > test-drop.R: Observed statistic(s) edgecov.-samplike.m are at their smallest attainable values. Their coefficients will be fixed at -Inf. > test-drop.R: All terms are either offsets or extreme values. No optimization is performed. > test-drop.R: Evaluating log-likelihood at the estimate. > test-drop.R: > test-drop.R: Observed statistic(s) edgecov.samplike.m are at their greatest attainable values. Their coefficients will be fixed at +Inf. > test-drop.R: Starting maximum pseudolikelihood estimation (MPLE): > test-drop.R: Obtaining the responsible dyads. > test-drop.R: Evaluating the predictor and response matrix. > test-drop.R: Maximizing the pseudolikelihood. > test-drop.R: Finished MPLE. > test-drop.R: Evaluating log-likelihood at the estimate. > test-constraints.R: Starting maximum pseudolikelihood estimation (MPLE): > test-constraints.R: Obtaining the responsible dyads. > test-constraints.R: Evaluating the predictor and response matrix. > test-drop.R: > test-constraints.R: Maximizing the pseudolikelihood. > test-constraints.R: Finished MPLE. > test-constraints.R: Evaluating log-likelihood at the estimate. > test-drop.R: Observed statistic(s) edgecov.samplike.m are at their greatest attainable values. Their coefficients will be fixed at +Inf. > test-drop.R: Starting maximum pseudolikelihood estimation (MPLE): > test-drop.R: Obtaining the responsible dyads. > test-drop.R: Evaluating the predictor and response matrix. > test-drop.R: Maximizing the pseudolikelihood. > test-drop.R: Finished MPLE. > test-drop.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-drop.R: Iteration 1 of at most 10: > test-drop.R: 1 > test-drop.R: Optimizing with step length 1.0000. > test-drop.R: The log-likelihood improved by 0.0001. > test-drop.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-drop.R: Finished MCMLE. > test-drop.R: Evaluating log-likelihood at the estimate. > test-drop.R: Fitting the dyad-independent submodel... > test-drop.R: Bridging between the dyad-independent submodel and the full model... > test-drop.R: Setting up bridge sampling... > test-constraints.R: Starting maximum pseudolikelihood estimation (MPLE): > test-constraints.R: Obtaining the responsible dyads. > test-constraints.R: Evaluating the predictor and response matrix. > test-constraints.R: Maximizing the pseudolikelihood. > test-constraints.R: Finished MPLE. > test-constraints.R: Evaluating log-likelihood at the estimate. > test-drop.R: Using 16 bridges: > test-drop.R: 1 > test-drop.R: 2 > test-drop.R: 3 > test-drop.R: 4 > test-drop.R: 5 > test-drop.R: 6 > test-drop.R: 7 > test-drop.R: 8 > test-drop.R: 9 > test-drop.R: 10 > test-drop.R: 11 > test-drop.R: 12 > test-drop.R: 13 > test-drop.R: 14 > test-drop.R: 15 > test-drop.R: 16 > test-drop.R: . > test-drop.R: Bridging finished. > test-drop.R: > test-drop.R: This model was fit using MCMC. To examine model diagnostics and check > test-drop.R: for degeneracy, use the mcmc.diagnostics() function. > test-drop.R: Observed statistic(s) edgecov.-samplike.m are at their smallest attainable values. Their coefficients will be fixed at -Inf. > test-drop.R: Starting maximum pseudolikelihood estimation (MPLE): > test-drop.R: Obtaining the responsible dyads. > test-drop.R: Evaluating the predictor and response matrix. > test-drop.R: Maximizing the pseudolikelihood. > test-drop.R: Finished MPLE. > test-drop.R: Evaluating log-likelihood at the estimate. > test-drop.R: > test-drop.R: Observed statistic(s) edgecov.-samplike.m are at their smallest attainable values. Their coefficients will be fixed at -Inf. > test-drop.R: Starting maximum pseudolikelihood estimation (MPLE): > test-drop.R: Obtaining the responsible dyads. > test-drop.R: Evaluating the predictor and response matrix. > test-drop.R: Maximizing the pseudolikelihood. > test-drop.R: Finished MPLE. > test-drop.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-drop.R: Iteration 1 of at most 10: > test-drop.R: 1 Optimizing with step length 1.0000. > test-drop.R: The log-likelihood improved by 0.0068. > test-drop.R: Convergence test p-value: < 0.0001. > test-drop.R: Converged with 99% confidence. > test-drop.R: Finished MCMLE. > test-drop.R: Evaluating log-likelihood at the estimate. > test-drop.R: Fitting the dyad-independent submodel... > test-constraints.R: Starting maximum pseudolikelihood estimation (MPLE): > test-constraints.R: Obtaining the responsible dyads. > test-constraints.R: Evaluating the predictor and response matrix. > test-constraints.R: Maximizing the pseudolikelihood. > test-constraints.R: Finished MPLE. > test-constraints.R: Evaluating log-likelihood at the estimate. > test-constraints.R: > test-drop.R: Bridging between the dyad-independent submodel and the full model... > test-drop.R: Setting up bridge sampling... > test-drop.R: Using 16 bridges: 1 > test-drop.R: 2 > test-drop.R: 3 > test-drop.R: 4 > test-drop.R: 5 > test-drop.R: 6 > test-drop.R: 7 > test-drop.R: 8 > test-drop.R: 9 > test-drop.R: 10 > test-drop.R: 11 > test-drop.R: 12 > test-drop.R: 13 > test-drop.R: 14 > test-constraints.R: Starting maximum pseudolikelihood estimation (MPLE): > test-constraints.R: Obtaining the responsible dyads. > test-constraints.R: Evaluating the predictor and response matrix. > test-constraints.R: Maximizing the pseudolikelihood. > test-constraints.R: Finished MPLE. > test-drop.R: 15 > test-constraints.R: Evaluating log-likelihood at the estimate. > test-drop.R: 16 > test-drop.R: . > test-drop.R: Bridging finished. > test-drop.R: > test-drop.R: This model was fit using MCMC. To examine model diagnostics and check > test-drop.R: for degeneracy, use the mcmc.diagnostics() function. > test-drop.R: Evaluating network in model. > test-drop.R: Initializing unconstrained Metropolis-Hastings proposal: > test-drop.R: 'ergm:MH_SPDyad'. > test-drop.R: Initializing model... > test-drop.R: Model initialized. > test-drop.R: Using initial method 'MPLE'. > test-drop.R: Initial parameters provided by caller: None. > test-drop.R: number of free parameters: 7 > test-drop.R: number of fixed parameters: 0 > test-drop.R: Observed statistic(s) triangle and kstar5 are at their smallest attainable values. Their coefficients will be fixed at -Inf. > test-drop.R: Fitting initial model. > test-drop.R: Starting maximum pseudolikelihood estimation (MPLE): > test-drop.R: Obtaining the responsible dyads. > test-drop.R: Evaluating the predictor and response matrix. > test-drop.R: Maximizing the pseudolikelihood. > test-drop.R: Finished MPLE. > test-drop.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-drop.R: Density guard set to 10000 from an initial count of 3 edges. > test-drop.R: > test-drop.R: Iteration 1 of at most 3 with parameter: > test-drop.R: edges triangle degree2 kstar2 kstar5 > test-drop.R: 1.313175 -Inf 6.009173 -4.163221 -Inf > test-drop.R: gwdegree gwdegree.decay > test-drop.R: -2.860022 1.666671 > test-drop.R: Starting unconstrained MCMC... > test-constraints.R: Starting maximum pseudolikelihood estimation (MPLE): > test-constraints.R: Obtaining the responsible dyads. > test-constraints.R: Evaluating the predictor and response matrix. > test-constraints.R: Maximizing the pseudolikelihood. > test-constraints.R: Finished MPLE. > test-constraints.R: Evaluating log-likelihood at the estimate. > test-constraints.R: > test-drop.R: Back from unconstrained MCMC. > test-constraints.R: Best valid proposal 'ConstantEdges' cannot take into account hint(s) 'triadic'. > test-drop.R: New interval = 512. > test-drop.R: Average estimating function values: > test-drop.R: edges degree2 kstar2 gwdegree gwdegree.decay > test-drop.R: -0.29629630 -0.11934156 -0.11934156 -0.57005199 0.06446661 > test-drop.R: Starting MCMLE Optimization... > test-drop.R: 1 > test-drop.R: Optimizing with step length 1.0000. > test-drop.R: Using lognormal metric (see control.ergm function). > test-drop.R: Optimizing loglikelihood > test-constraints.R: All terms are either offsets or extreme values. No optimization is performed. > test-constraints.R: Evaluating log-likelihood at the estimate. > test-constraints.R: Setting up bridge sampling... > test-drop.R: Starting MCMC s.e. computation. > test-constraints.R: Best valid proposal 'ConstantEdges' cannot take into account hint(s) 'triadic'. > test-drop.R: The log-likelihood improved by 0.0385. > test-constraints.R: Using 16 bridges: > test-constraints.R: 1 > test-drop.R: Estimated covariance matrix of the statistics has nullity 3. Effective parameter number adjusted to 2. > test-drop.R: Test statistic: T^2 = 21.94136, with 2 free parameter(s) and 240.9959 degrees of freedom. > test-drop.R: Convergence test p-value: < 0.0001. > test-drop.R: Converged with 99% confidence. > test-drop.R: Finished MCMLE. > test-drop.R: Evaluating log-likelihood at the estimate. > test-drop.R: Initializing model to obtain the list of dyad-independent terms... > test-constraints.R: 2 > test-drop.R: Fitting the dyad-independent submodel... > test-constraints.R: 3 > test-constraints.R: 4 > test-drop.R: Dyad-independent submodel MLE has likelihood -11.02185 at: > test-drop.R: [1] -2.639057 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 > test-drop.R: [8] 0.000000 > test-drop.R: Bridging between the dyad-independent submodel and the full model... > test-drop.R: Setting up bridge sampling... > test-drop.R: Initializing model and proposals... > test-constraints.R: 5 > test-constraints.R: 6 > test-drop.R: Model and proposals initialized. > test-drop.R: Using 16 bridges: Running theta=[-1.175967, -Inf, 6.177257,-4.435272, -Inf,-1.386658, 1.950625, 0.000000]. > test-drop.R: Running theta=[-1.270360, -Inf, 5.778724,-4.149126, -Inf,-1.297196, 1.824778, 0.000000]. > test-drop.R: Running theta=[-1.364753, -Inf, 5.380191,-3.862979, -Inf,-1.207735, 1.698931, 0.000000]. > test-drop.R: Running theta=[-1.459146, -Inf, 4.981659,-3.576833, -Inf,-1.118273, 1.573085, 0.000000]. > test-drop.R: Running theta=[-1.553539, -Inf, 4.583126,-3.290686, -Inf,-1.028811, 1.447238, 0.000000]. > test-constraints.R: 7 > test-drop.R: Running theta=[-1.6479316, -Inf, 4.1845933,-3.0045393, -Inf,-0.9393491, 1.3213911, 0.0000000]. > test-drop.R: Running theta=[-1.7423245, -Inf, 3.7860606,-2.7183927, -Inf,-0.8498873, 1.1955444, 0.0000000]. > test-drop.R: Running theta=[-1.8367174, -Inf, 3.3875279,-2.4322461, -Inf,-0.7604255, 1.0696976, 0.0000000]. > test-constraints.R: 8 > test-drop.R: Running theta=[-1.9311104, -Inf, 2.9889952,-2.1460995, -Inf,-0.6709636, 0.9438508, 0.0000000]. > test-drop.R: Running theta=[-2.0255033, -Inf, 2.5904625,-1.8599529, -Inf,-0.5815018, 0.8180040, 0.0000000]. > test-drop.R: Running theta=[-2.1198962, -Inf, 2.1919298,-1.5738063, -Inf,-0.4920400, 0.6921573, 0.0000000]. > test-drop.R: Running theta=[-2.2142891, -Inf, 1.7933971,-1.2876597, -Inf,-0.4025782, 0.5663105, 0.0000000]. > test-constraints.R: 9 > test-drop.R: Running theta=[-2.3086821, -Inf, 1.3948644,-1.0015131, -Inf,-0.3131164, 0.4404637, 0.0000000]. > test-drop.R: Running theta=[-2.4030750, -Inf, 0.9963317,-0.7153665, -Inf,-0.2236545, 0.3146169, 0.0000000]. > test-drop.R: Running theta=[-2.4974679, -Inf, 0.5977990,-0.4292199, -Inf,-0.1341927, 0.1887702, 0.0000000]. > test-drop.R: Running theta=[-2.59186086, -Inf, 0.19926635,-0.14307330, -Inf,-0.04473091, 0.06292339, 0.00000000]. > test-constraints.R: 10 > test-drop.R: . > test-drop.R: Bridge sampling finished. Collating... > test-drop.R: Bridging finished. > test-drop.R: > test-drop.R: This model was fit using MCMC. To examine model diagnostics and check > test-drop.R: for degeneracy, use the mcmc.diagnostics() function. > test-constraints.R: 11 > test-constraints.R: 12 > test-constraints.R: 13 > test-constraints.R: 14 > test-constraints.R: 15 > test-constraints.R: 16 > test-constraints.R: . > test-constraints.R: Note: The constraint on the sample space is not dyad-independent. Null > test-constraints.R: model likelihood is only implemented for dyad-independent > test-constraints.R: constraints > test-constraints.R: at this time. Number of observations is similarly poorly defined. This > test-constraints.R: means that all likelihood-based inference (LRT, Analysis of Deviance, > test-constraints.R: AIC, BIC, etc.) is only valid between models with the same reference > test-constraints.R: distribution and constraints. > test-constraints.R: > test-constraints.R: Best valid proposal 'CondDegree' cannot take into account hint(s) 'triadic'. > test-constraints.R: Model and/or observational constraints are not dyad-independent. Dyad imputation cannot be used. Please ensure your LHS network satisfies all constraints. > test-ergm-proposal-unload.R: > test-ergm-proposal-unload.R: 'ergm.count' 4.1.3 (2025-09-10), part of the Statnet Project > test-ergm-proposal-unload.R: * 'news(package="ergm.count")' for changes since last version > test-ergm-proposal-unload.R: * 'citation("ergm.count")' for citation information > test-ergm-proposal-unload.R: * 'https://statnet.org' for help, support, and other information > test-ergm-proposal-unload.R: > test-constraints.R: Starting contrastive divergence estimation via CD-MCMLE: > test-constraints.R: Iteration 1 of at most 60: > test-constraints.R: Convergence test P-value:1.1e-05 > test-constraints.R: 1 > test-constraints.R: The log-likelihood improved by 0.07919. > test-constraints.R: Iteration 2 of at most 60: > test-constraints.R: Convergence test P-value:3.7e-02 > test-constraints.R: 1 > test-constraints.R: The log-likelihood improved by 0.01687. > test-constraints.R: Iteration 3 of at most 60: > test-constraints.R: Convergence test P-value:7e-01 > test-constraints.R: Convergence detected. Stopping. > test-constraints.R: 1 > test-constraints.R: The log-likelihood improved by 0.0006029. > test-constraints.R: Finished CD. > test-constraints.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-constraints.R: Iteration 1 of at most 60: > test-constraints.R: 1 > test-constraints.R: Optimizing with step length 1.0000. > test-constraints.R: The log-likelihood improved by 0.0263. > test-constraints.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-constraints.R: Finished MCMLE. > test-constraints.R: Evaluating log-likelihood at the estimate. > test-constraints.R: Setting up bridge sampling... > test-constraints.R: Best valid proposal 'CondDegree' cannot take into account hint(s) 'triadic'. > test-constraints.R: Using 16 bridges: 1 > test-constraints.R: 2 > test-constraints.R: 3 > test-constraints.R: 4 > test-constraints.R: 5 > test-constraints.R: 6 > test-constraints.R: 7 > test-constraints.R: 8 > test-constraints.R: 9 > test-constraints.R: 10 > test-constraints.R: 11 > test-constraints.R: 12 > test-constraints.R: 13 > test-constraints.R: 14 > test-ergm-san.R: Best valid proposal 'Unif' cannot take into account hint(s) 'sparse' and 'triadic'. > test-constraints.R: 15 > test-constraints.R: 16 > test-constraints.R: . > test-constraints.R: Note: The constraint on the sample space is not dyad-independent. Null > test-constraints.R: model likelihood is only implemented for dyad-independent constraints > test-constraints.R: at this time. Number of observations is similarly poorly defined. This > test-constraints.R: means that all likelihood-based inference (LRT, Analysis of Deviance, > test-constraints.R: AIC, BIC, etc.) is only valid between models with the same reference > test-constraints.R: distribution and constraints. > test-constraints.R: > test-constraints.R: This model was fit using MCMC. To examine model diagnostics and check > test-constraints.R: for degeneracy, use the mcmc.diagnostics() function. > test-ergm-term-doc.R: > test-ergm-term-doc.R: 'ergm.count' 4.1.3 (2025-09-10), part of the Statnet Project > test-ergm-term-doc.R: * 'news(package="ergm.count")' for changes since last version > test-ergm-term-doc.R: * 'citation("ergm.count")' for citation information > test-ergm-term-doc.R: * 'https://statnet.org' for help, support, and other information > test-ergm-term-doc.R: > test-ergm-term-doc.R: Found 9 matching ergm terms: > test-ergm-term-doc.R: Symmetrize(formula, rule="weak") (binary, valued) > test-ergm-term-doc.R: Evaluation on symmetrized (undirected) network > test-ergm-term-doc.R: > test-ergm-term-doc.R: ctriple(attr=NULL, diff=FALSE, levels=NULL) (binary) > test-ergm-term-doc.R: ctriad (binary) > test-ergm-term-doc.R: Cyclic triples > test-ergm-term-doc.R: > test-ergm-term-doc.R: localtriangle(x) (binary) > test-ergm-term-doc.R: Triangles within neighborhoods > test-ergm-term-doc.R: > test-ergm-term-doc.R: nodemix(attr, base=NULL, b1levels=NULL, b2levels=NULL, levels=NULL, levels2=-1) (binary) > test-ergm-term-doc.R: nodemix(attr, base=NULL, b1levels=NULL, b2levels=NULL, levels=NULL, levels2=-1, form="sum") (valued) > test-ergm-term-doc.R: Nodal attribute mixing > test-ergm-term-doc.R: > test-ergm-term-doc.R: opentriad (binary) > test-ergm-term-doc.R: Open triads > test-ergm-term-doc.R: > test-ergm-term-doc.R: threetrail(keep=NULL, levels=NULL) (binary) > test-ergm-term-doc.R: threepath(keep=NULL, levels=NULL) (binary) > test-ergm-term-doc.R: Three-trails > test-ergm-term-doc.R: > test-ergm-term-doc.R: triangle(attr=NULL, diff=FALSE, levels=NULL) (binary) > test-ergm-term-doc.R: triangles(attr=NULL, diff=FALSE, levels=NULL) (binary) > test-ergm-term-doc.R: Triangles > test-ergm-term-doc.R: > test-ergm-term-doc.R: tripercent(attr=NULL, diff=FALSE, levels=NULL) (binary) > test-ergm-term-doc.R: Triangle percentage > test-ergm-term-doc.R: > test-ergm-term-doc.R: ttriple(attr=NULL, diff=FALSE, levels=NULL) (binary) > test-ergm-term-doc.R: ttriad (binary) > test-ergm-term-doc.R: Transitive triples > test-ergm-term-doc.R: Found 31 matching ergm terms: > test-ergm-term-doc.R: b1concurrent(by=NULL, levels=NULL) (binary) > test-ergm-term-doc.R: Concurrent node count for the first mode in a bipartite network > test-ergm-term-doc.R: > test-ergm-term-doc.R: b1cov(attr) (binary) > test-ergm-term-doc.R: b1cov(attr, form="sum") (valued) > test-ergm-term-doc.R: Main effect of a covariate for the first mode in a bipartite network > test-ergm-term-doc.R: > test-ergm-term-doc.R: b1degrange(from, to=`+Inf`, by=NULL, homophily=FALSE, levels=NULL) (binary) > test-ergm-term-doc.R: Degree range for the first mode in a bipartite network > test-ergm-term-doc.R: > test-ergm-term-doc.R: b1degree(d, by=NULL, levels=NULL) (binary) > test-ergm-term-doc.R: Degree for the first mode in a bipartite network > test-ergm-term-doc.R: > test-ergm-term-doc.R: b1dsp(d) (binary) > test-ergm-term-doc.R: Dyadwise shared partners for dyads in the first bipartition > test-ergm-term-doc.R: > test-ergm-term-doc.R: b1factor(attr, base=1, levels=-1) (binary) > test-ergm-term-doc.R: b1factor(attr, base=1, levels=-1, form="sum") (valued) > test-ergm-term-doc.R: Factor attribute effect for the first mode in a bipartite network > test-ergm-term-doc.R: > test-ergm-term-doc.R: b1mindegree(d) (binary) > test-ergm-term-doc.R: Minimum degree for the first mode in a bipartite network > test-ergm-term-doc.R: > test-ergm-term-doc.R: b1nodematch(attr, diff=FALSE, keep=NULL, alpha=1, beta=1, byb2attr=NULL, levels=NULL) (binary) > test-ergm-term-doc.R: Nodal attribute-based homophily effect for the first mode in a bipartite network > test-ergm-term-doc.R: > test-ergm-term-doc.R: b1sociality(nodes=-1) (binary) > test-ergm-term-doc.R: b1sociality(nodes=-1, form="sum") (valued) > test-ergm-term-doc.R: Degree > test-ergm-term-doc.R: > test-ergm-term-doc.R: b1star(k, attr=NULL, levels=NULL) (binary) > test-ergm-term-doc.R: k-stars for the first mode in a bipartite network > test-ergm-term-doc.R: > test-ergm-term-doc.R: b1starmix(k, attr, base=NULL, diff=TRUE) (binary) > test-ergm-term-doc.R: Mixing matrix for k-stars centered on the first mode of a bipartite network > test-ergm-term-doc.R: > test-ergm-term-doc.R: b1twostar(b1attr, b2attr, base=NULL, b1levels=NULL, b2levels=NULL, levels2=NULL) (binary) > test-ergm-term-doc.R: Two-star census for central nodes centered on the first mode of a bipartite network > test-ergm-term-doc.R: > test-ergm-term-doc.R: b2concurrent(by=NULL) (binary) > test-ergm-term-doc.R: Concurrent node count for the second mode in a bipartite network > test-ergm-term-doc.R: > test-ergm-term-doc.R: b2cov(attr) (binary) > test-ergm-term-doc.R: b2cov(attr, form="sum") (valued) > test-ergm-term-doc.R: Main effect of a covariate for the second mode in a bipartite network > test-ergm-term-doc.R: > test-ergm-term-doc.R: b2degrange(from, to=+Inf, by=NULL, homophily=FALSE, levels=NULL) (binary) > test-ergm-term-doc.R: Degree range for the second mode in a bipartite network > test-ergm-term-doc.R: > test-ergm-term-doc.R: b2degree(d, by=NULL) (binary) > test-ergm-term-doc.R: Degree for the second mode in a bipartite network > test-ergm-term-doc.R: > test-ergm-term-doc.R: b2dsp(d) (binary) > test-ergm-term-doc.R: Dyadwise shared partners for dyads in the second bipartition > test-ergm-term-doc.R: > test-ergm-term-doc.R: b2factor(attr, base=1, levels=-1) (binary) > test-ergm-term-doc.R: b2factor(attr, base=1, levels=-1, form="sum") (valued) > test-ergm-term-doc.R: Factor attribute effect for the second mode in a bipartite network > test-ergm-term-doc.R: > test-ergm-term-doc.R: b2mindegree(d) (binary) > test-ergm-term-doc.R: Minimum degree for the second mode in a bipartite network > test-ergm-term-doc.R: > test-ergm-term-doc.R: b2nodematch(attr, diff=FALSE, keep=NULL, alpha=1, beta=1, byb1attr=NULL, levels=NULL) (binary) > test-ergm-term-doc.R: Nodal attribute-based homophily effect for the second mode in a bipartite network > test-ergm-term-doc.R: > test-ergm-term-doc.R: b2sociality(nodes=-1) (binary) > test-ergm-term-doc.R: b2sociality(nodes=-1, form="sum") (valued) > test-ergm-term-doc.R: Degree > test-ergm-term-doc.R: > test-ergm-term-doc.R: b2star(k, attr=NULL, levels=NULL) (binary) > test-ergm-term-doc.R: k-stars for the second mode in a bipartite network > test-ergm-term-doc.R: > test-ergm-term-doc.R: b2starmix(k, attr, base=NULL, diff=TRUE) (binary) > test-ergm-term-doc.R: Mixing matrix for k-stars centered on the second mode of a bipartite network > test-ergm-term-doc.R: > test-ergm-term-doc.R: b2twostar(b1attr, b2attr, base=NULL, b1levels=NULL, b2levels=NULL, levels2=NULL) (binary) > test-ergm-term-doc.R: Two-star census for central nodes centered on the second mode of a bipartite network > test-ergm-term-doc.R: > test-ergm-term-doc.R: coincidence(levels=NULL,active=0) (binary) > test-ergm-term-doc.R: Coincident node count for the second mode in a bipartite (aka two-mode) network > test-ergm-term-doc.R: > test-ergm-term-doc.R: diff(attr, pow=1, dir="t-h", sign.action="identity") (binary) > test-ergm-term-doc.R: diff(attr, pow=1, dir="t-h", sign.action="identity", form ="sum") (valued) > test-ergm-term-doc.R: Difference > test-ergm-term-doc.R: > test-ergm-term-doc.R: gwb1degree(decay, fixed=FALSE, attr=NULL, cutoff=30, levels=NULL) (binary) > test-ergm-term-doc.R: Geometrically weighted degree distribution for the first mode in a bipartite network > test-ergm-term-doc.R: > test-ergm-term-doc.R: gwb1dsp(decay=0, fixed=FALSE, cutoff=30) (binary) > test-ergm-term-doc.R: Geometrically weighted dyadwise shared partner distribution for dyads in the first bipartition > test-ergm-term-doc.R: > test-ergm-term-doc.R: gwb2degree(decay, fixed=FALSE, attr=NULL, cutoff=30, levels=NULL) (binary) > test-ergm-term-doc.R: Geometrically weighted degree distribution for the second mode in a bipartite network > test-ergm-term-doc.R: > test-ergm-term-doc.R: gwb2dsp(decay=0, fixed=FALSE, cutoff=30) (binary) > test-ergm-term-doc.R: Geometrically weighted dyadwise shared partner distribution for dyads in the second bipartition > test-ergm-term-doc.R: > test-ergm-term-doc.R: isolatededges (binary) > test-ergm-term-doc.R: Isolated edges > test-ergm-term-doc.R: Found 36 matching ergm terms: > test-ergm-term-doc.R: Project(formula, mode) (binary) > test-ergm-term-doc.R: Proj1(formula) (binary) > test-ergm-term-doc.R: Proj2(formula) (binary) > test-ergm-term-doc.R: Evaluation on a projection of a bipartite network > test-ergm-term-doc.R: > test-ergm-term-doc.R: b1concurrent(by=NULL, levels=NULL) (binary) > test-ergm-term-doc.R: Concurrent node count for the first mode in a bipartite network > test-ergm-term-doc.R: > test-ergm-term-doc.R: b1cov(attr) (binary) > test-ergm-term-doc.R: b1cov(attr, form="sum") (valued) > test-ergm-term-doc.R: Main effect of a covariate for the first mode in a bipartite network > test-ergm-term-doc.R: > test-ergm-term-doc.R: nodecovrange(attr) (binary) > test-ergm-term-doc.R: Range of covariate values for neighbors of a mode-1 node > test-ergm-term-doc.R: > test-ergm-term-doc.R: b1degrange(from, to=`+Inf`, by=NULL, homophily=FALSE, levels=NULL) (binary) > test-ergm-term-doc.R: Degree range for the first mode in a bipartite network > test-ergm-term-doc.R: > test-ergm-term-doc.R: b1degree(d, by=NULL, levels=NULL) (binary) > test-ergm-term-doc.R: Degree for the first mode in a bipartite network > test-ergm-term-doc.R: > test-ergm-term-doc.R: b1dsp(d) (binary) > test-ergm-term-doc.R: Dyadwise shared partners for dyads in the first bipartition > test-ergm-term-doc.R: > test-ergm-term-doc.R: b1factor(attr, base=1, levels=-1) (binary) > test-ergm-term-doc.R: b1factor(attr, base=1, levels=-1, form="sum") (valued) > test-ergm-term-doc.R: Factor attribute effect for the first mode in a bipartite network > test-ergm-term-doc.R: > test-ergm-term-doc.R: b1factordistinct(attr, levels=TRUE) (binary) > test-ergm-term-doc.R: Number of distinct neighbor types for the first node > test-ergm-term-doc.R: > test-ergm-term-doc.R: b1mindegree(d) (binary) > test-ergm-term-doc.R: Minimum degree for the first mode in a bipartite network > test-ergm-term-doc.R: > test-ergm-term-doc.R: b1nodematch(attr, diff=FALSE, keep=NULL, alpha=1, beta=1, byb2attr=NULL, levels=NULL) (binary) > test-ergm-term-doc.R: Nodal attribute-based homophily effect for the first mode in a bipartite network > test-ergm-term-doc.R: > test-ergm-term-doc.R: b1sociality(nodes=-1) (binary) > test-ergm-term-doc.R: b1sociality(nodes=-1, form="sum") (valued) > test-ergm-term-doc.R: Degree > test-ergm-term-doc.R: > test-ergm-term-doc.R: b1star(k, attr=NULL, levels=NULL) (binary) > test-ergm-term-doc.R: k-stars for the first mode in a bipartite network > test-ergm-term-doc.R: > test-ergm-term-doc.R: b1starmix(k, attr, base=NULL, diff=TRUE) (binary) > test-ergm-term-doc.R: Mixing matrix for k-stars centered on the first mode of a bipartite network > test-ergm-term-doc.R: > test-ergm-term-doc.R: b1twostar(b1attr, b2attr, base=NULL, b1levels=NULL, b2levels=NULL, levels2=NULL) (binary) > test-ergm-term-doc.R: Two-star census for central nodes centered on the first mode of a bipartite network > test-ergm-term-doc.R: > test-ergm-term-doc.R: b2concurrent(by=NULL) (binary) > test-ergm-term-doc.R: Concurrent node count for the second mode in a bipartite network > test-ergm-term-doc.R: > test-ergm-term-doc.R: b2cov(attr) (binary) > test-ergm-term-doc.R: b2cov(attr, form="sum") (valued) > test-ergm-term-doc.R: Main effect of a covariate for the second mode in a bipartite network > test-ergm-term-doc.R: > test-ergm-term-doc.R: nodecovrange(attr) (binary) > test-ergm-term-doc.R: Range of covariate values for neighbors of a mode-2 node > test-ergm-term-doc.R: > test-ergm-term-doc.R: b2degrange(from, to=+Inf, by=NULL, homophily=FALSE, levels=NULL) (binary) > test-ergm-term-doc.R: Degree range for the second mode in a bipartite network > test-ergm-term-doc.R: > test-ergm-term-doc.R: b2degree(d, by=NULL) (binary) > test-ergm-term-doc.R: Degree for the second mode in a bipartite network > test-ergm-term-doc.R: > test-ergm-term-doc.R: b2dsp(d) (binary) > test-ergm-term-doc.R: Dyadwise shared partners for dyads in the second bipartition > test-ergm-term-doc.R: > test-ergm-term-doc.R: b2factor(attr, base=1, levels=-1) (binary) > test-ergm-term-doc.R: b2factor(attr, base=1, levels=-1, form="sum") (valued) > test-ergm-term-doc.R: Factor attribute effect for the second mode in a bipartite network > test-ergm-term-doc.R: > test-ergm-term-doc.R: b2factordistinct(attr, levels=TRUE) (binary) > test-ergm-term-doc.R: Number of distinct neighbor types for the second mode > test-ergm-term-doc.R: > test-ergm-term-doc.R: b2mindegree(d) (binary) > test-ergm-term-doc.R: Minimum degree for the second mode in a bipartite network > test-ergm-term-doc.R: > test-ergm-term-doc.R: b2nodematch(attr, diff=FALSE, keep=NULL, alpha=1, beta=1, byb1attr=NULL, levels=NULL) (binary) > test-ergm-term-doc.R: Nodal attribute-based homophily effect for the second mode in a bipartite network > test-ergm-term-doc.R: > test-ergm-term-doc.R: b2sociality(nodes=-1) (binary) > test-ergm-term-doc.R: b2sociality(nodes=-1, form="sum") (valued) > test-ergm-term-doc.R: Degree > test-ergm-term-doc.R: > test-ergm-term-doc.R: b2star(k, attr=NULL, levels=NULL) (binary) > test-ergm-term-doc.R: k-stars for the second mode in a bipartite network > test-ergm-term-doc.R: > test-ergm-term-doc.R: b2starmix(k, attr, base=NULL, diff=TRUE) (binary) > test-ergm-term-doc.R: Mixing matrix for k-stars centered on the second mode of a bipartite network > test-ergm-term-doc.R: > test-ergm-term-doc.R: b2twostar(b1attr, b2attr, base=NULL, b1levels=NULL, b2levels=NULL, levels2=NULL) (binary) > test-ergm-term-doc.R: Two-star census for central nodes centered on the second mode of a bipartite network > test-ergm-term-doc.R: > test-ergm-term-doc.R: coincidence(levels=NULL,active=0) (binary) > test-ergm-term-doc.R: Coincident node count for the second mode in a bipartite (aka two-mode) network > test-ergm-term-doc.R: > test-ergm-term-doc.R: diff(attr, pow=1, dir="t-h", sign.action="identity") (binary) > test-ergm-term-doc.R: diff(attr, pow=1, dir="t-h", sign.action="identity", form ="sum") (valued) > test-ergm-term-doc.R: Difference > test-ergm-term-doc.R: > test-ergm-term-doc.R: gwb1degree(decay, fixed=FALSE, attr=NULL, cutoff=30, levels=NULL) (binary) > test-ergm-term-doc.R: Geometrically weighted degree distribution for the first mode in a bipartite network > test-ergm-term-doc.R: > test-ergm-term-doc.R: gwb1dsp(decay=0, fixed=FALSE, cutoff=30) (binary) > test-ergm-term-doc.R: Geometrically weighted dyadwise shared partner distribution for dyads in the first bipartition > test-ergm-term-doc.R: > test-ergm-term-doc.R: gwb2degree(decay, fixed=FALSE, attr=NULL, cutoff=30, levels=NULL) (binary) > test-ergm-term-doc.R: Geometrically weighted degree distribution for the second mode in a bipartite network > test-ergm-term-doc.R: > test-ergm-term-doc.R: gwb2dsp(decay=0, fixed=FALSE, cutoff=30) (binary) > test-ergm-term-doc.R: Geometrically weighted dyadwise shared partner distribution for dyads in the second bipartition > test-ergm-term-doc.R: > test-ergm-term-doc.R: isolatededges (binary) > test-ergm-term-doc.R: Isolated edges > test-ergm-term-doc.R: Definitions for term(s) b2factor : > test-ergm-term-doc.R: b2factor(attr, base=1, levels=-1) > test-ergm-term-doc.R: Factor attribute effect for the second mode in a bipartite network: This term adds multiple network statistics to the model, one for each of (a subset of) the > test-ergm-term-doc.R: unique values of the attr attribute. Each of these statistics > test-ergm-term-doc.R: gives the number of times a node with that attribute in the second mode of > test-ergm-term-doc.R: the network appears in an edge. The second mode of a bipartite network > test-ergm-term-doc.R: object is sometimes known as the "event" mode. > test-ergm-term-doc.R: Keywords: bipartite, categorical nodal attribute, dyad-independent, frequently-used, undirected, binary, valued > test-ergm-term-doc.R: > test-ergm-term-doc.R: No terms named 'b3factor' were found. Try searching with search='b3factor'instead. > test-ergm-term-doc.R: Found 36 matching ergm terms: > test-ergm-term-doc.R: Project(formula, mode) (binary) > test-ergm-term-doc.R: Proj1(formula) (binary) > test-ergm-term-doc.R: Proj2(formula) (binary) > test-ergm-term-doc.R: Evaluation on a projection of a bipartite network > test-ergm-term-doc.R: > test-ergm-term-doc.R: b1concurrent(by=NULL, levels=NULL) (binary) > test-ergm-term-doc.R: Concurrent node count for the first mode in a bipartite network > test-ergm-term-doc.R: > test-ergm-term-doc.R: b1cov(attr) (binary) > test-ergm-term-doc.R: b1cov(attr, form="sum") (valued) > test-ergm-term-doc.R: Main effect of a covariate for the first mode in a bipartite network > test-ergm-term-doc.R: > test-ergm-term-doc.R: nodecovrange(attr) (binary) > test-ergm-term-doc.R: Range of covariate values for neighbors of a mode-1 node > test-ergm-term-doc.R: > test-ergm-term-doc.R: b1degrange(from, to=`+Inf`, by=NULL, homophily=FALSE, levels=NULL) (binary) > test-ergm-term-doc.R: Degree range for the first mode in a bipartite network > test-ergm-term-doc.R: > test-ergm-term-doc.R: b1degree(d, by=NULL, levels=NULL) (binary) > test-ergm-term-doc.R: Degree for the first mode in a bipartite network > test-ergm-term-doc.R: > test-ergm-term-doc.R: b1dsp(d) (binary) > test-ergm-term-doc.R: Dyadwise shared partners for dyads in the first bipartition > test-ergm-term-doc.R: > test-ergm-term-doc.R: b1factor(attr, base=1, levels=-1) (binary) > test-ergm-term-doc.R: b1factor(attr, base=1, levels=-1, form="sum") (valued) > test-ergm-term-doc.R: Factor attribute effect for the first mode in a bipartite network > test-ergm-term-doc.R: > test-ergm-term-doc.R: b1factordistinct(attr, levels=TRUE) (binary) > test-ergm-term-doc.R: Number of distinct neighbor types for the first node > test-ergm-term-doc.R: > test-ergm-term-doc.R: b1mindegree(d) (binary) > test-ergm-term-doc.R: Minimum degree for the first mode in a bipartite network > test-ergm-term-doc.R: > test-ergm-term-doc.R: b1nodematch(attr, diff=FALSE, keep=NULL, alpha=1, beta=1, byb2attr=NULL, levels=NULL) (binary) > test-ergm-term-doc.R: Nodal attribute-based homophily effect for the first mode in a bipartite network > test-ergm-term-doc.R: > test-ergm-term-doc.R: b1sociality(nodes=-1) (binary) > test-ergm-term-doc.R: b1sociality(nodes=-1, form="sum") (valued) > test-ergm-term-doc.R: Degree > test-ergm-term-doc.R: > test-ergm-term-doc.R: b1star(k, attr=NULL, levels=NULL) (binary) > test-ergm-term-doc.R: k-stars for the first mode in a bipartite network > test-ergm-term-doc.R: > test-ergm-term-doc.R: b1starmix(k, attr, base=NULL, diff=TRUE) (binary) > test-ergm-term-doc.R: Mixing matrix for k-stars centered on the first mode of a bipartite network > test-ergm-term-doc.R: > test-ergm-term-doc.R: b1twostar(b1attr, b2attr, base=NULL, b1levels=NULL, b2levels=NULL, levels2=NULL) (binary) > test-ergm-term-doc.R: Two-star census for central nodes centered on the first mode of a bipartite network > test-ergm-term-doc.R: > test-ergm-term-doc.R: b2concurrent(by=NULL) (binary) > test-ergm-term-doc.R: Concurrent node count for the second mode in a bipartite network > test-ergm-term-doc.R: > test-ergm-term-doc.R: b2cov(attr) (binary) > test-ergm-term-doc.R: b2cov(attr, form="sum") (valued) > test-ergm-term-doc.R: Main effect of a covariate for the second mode in a bipartite network > test-ergm-term-doc.R: > test-ergm-term-doc.R: nodecovrange(attr) (binary) > test-ergm-term-doc.R: Range of covariate values for neighbors of a mode-2 node > test-ergm-term-doc.R: > test-ergm-term-doc.R: b2degrange(from, to=+Inf, by=NULL, homophily=FALSE, levels=NULL) (binary) > test-ergm-term-doc.R: Degree range for the second mode in a bipartite network > test-ergm-term-doc.R: > test-ergm-term-doc.R: b2degree(d, by=NULL) (binary) > test-ergm-term-doc.R: Degree for the second mode in a bipartite network > test-ergm-term-doc.R: > test-ergm-term-doc.R: b2dsp(d) (binary) > test-ergm-term-doc.R: Dyadwise shared partners for dyads in the second bipartition > test-ergm-term-doc.R: > test-ergm-term-doc.R: b2factor(attr, base=1, levels=-1) (binary) > test-ergm-term-doc.R: b2factor(attr, base=1, levels=-1, form="sum") (valued) > test-ergm-term-doc.R: Factor attribute effect for the second mode in a bipartite network > test-ergm-term-doc.R: > test-ergm-term-doc.R: b2factordistinct(attr, levels=TRUE) (binary) > test-ergm-term-doc.R: Number of distinct neighbor types for the second mode > test-ergm-term-doc.R: > test-ergm-term-doc.R: b2mindegree(d) (binary) > test-ergm-term-doc.R: Minimum degree for the second mode in a bipartite network > test-ergm-term-doc.R: > test-ergm-term-doc.R: b2nodematch(attr, diff=FALSE, keep=NULL, alpha=1, beta=1, byb1attr=NULL, levels=NULL) (binary) > test-ergm-term-doc.R: Nodal attribute-based homophily effect for the second mode in a bipartite network > test-ergm-term-doc.R: > test-ergm-term-doc.R: b2sociality(nodes=-1) (binary) > test-ergm-term-doc.R: b2sociality(nodes=-1, form="sum") (valued) > test-ergm-term-doc.R: Degree > test-ergm-term-doc.R: > test-ergm-term-doc.R: b2star(k, attr=NULL, levels=NULL) (binary) > test-ergm-term-doc.R: k-stars for the second mode in a bipartite network > test-ergm-term-doc.R: > test-ergm-term-doc.R: b2starmix(k, attr, base=NULL, diff=TRUE) (binary) > test-ergm-term-doc.R: Mixing matrix for k-stars centered on the second mode of a bipartite network > test-ergm-term-doc.R: > test-ergm-term-doc.R: b2twostar(b1attr, b2attr, base=NULL, b1levels=NULL, b2levels=NULL, levels2=NULL) (binary) > test-ergm-term-doc.R: Two-star census for central nodes centered on the second mode of a bipartite network > test-ergm-term-doc.R: > test-ergm-term-doc.R: coincidence(levels=NULL,active=0) (binary) > test-ergm-term-doc.R: Coincident node count for the second mode in a bipartite (aka two-mode) network > test-ergm-term-doc.R: > test-ergm-term-doc.R: diff(attr, pow=1, dir="t-h", sign.action="identity") (binary) > test-ergm-term-doc.R: diff(attr, pow=1, dir="t-h", sign.action="identity", form ="sum") (valued) > test-ergm-term-doc.R: Difference > test-ergm-term-doc.R: > test-ergm-term-doc.R: gwb1degree(decay, fixed=FALSE, attr=NULL, cutoff=30, levels=NULL) (binary) > test-ergm-term-doc.R: Geometrically weighted degree distribution for the first mode in a bipartite network > test-ergm-term-doc.R: > test-ergm-term-doc.R: gwb1dsp(decay=0, fixed=FALSE, cutoff=30) (binary) > test-ergm-term-doc.R: Geometrically weighted dyadwise shared partner distribution for dyads in the first bipartition > test-ergm-term-doc.R: > test-ergm-term-doc.R: gwb2degree(decay, fixed=FALSE, attr=NULL, cutoff=30, levels=NULL) (binary) > test-ergm-term-doc.R: Geometrically weighted degree distribution for the second mode in a bipartite network > test-ergm-term-doc.R: > test-ergm-term-doc.R: gwb2dsp(decay=0, fixed=FALSE, cutoff=30) (binary) > test-ergm-term-doc.R: Geometrically weighted dyadwise shared partner distribution for dyads in the second bipartition > test-ergm-term-doc.R: > test-ergm-term-doc.R: isolatededges (binary) > test-ergm-term-doc.R: Isolated edges > test-ergm-term-doc.R: Found 48 matching ergm terms: > test-ergm-term-doc.R: B(formula, form) (valued) > test-ergm-term-doc.R: Wrap binary terms for use in valued models > test-ergm-term-doc.R: > test-ergm-term-doc.R: Curve(formula, params, map, gradient=NULL, minpar=-Inf, maxpar=+Inf, cov=NULL) (valued) > test-ergm-term-doc.R: Parametrise(formula, params, map, gradient=NULL, minpar=-Inf, maxpar=+Inf, cov=NULL) (valued) > test-ergm-term-doc.R: Parametrize(formula, params, map, gradient=NULL, minpar=-Inf, maxpar=+Inf, cov=NULL) (valued) > test-ergm-term-doc.R: Impose a curved structure on term parameters > test-ergm-term-doc.R: > test-ergm-term-doc.R: Exp(formula) (valued) > test-ergm-term-doc.R: Exponentiate a network's statistic > test-ergm-term-doc.R: > test-ergm-term-doc.R: For(...) (valued) > test-ergm-term-doc.R: A for operator for terms > test-ergm-term-doc.R: > test-ergm-term-doc.R: Label(formula, label, pos) (valued) > test-ergm-term-doc.R: Modify terms' coefficient names > test-ergm-term-doc.R: > test-ergm-term-doc.R: Log(formula, log0=-1/sqrt(.Machine$double.eps)) (valued) > test-ergm-term-doc.R: Take a natural logarithm of a network's statistic > test-ergm-term-doc.R: > test-ergm-term-doc.R: Prod(formulas, label) (valued) > test-ergm-term-doc.R: A product (or an arbitrary power combination) of one or more formulas > test-ergm-term-doc.R: > test-ergm-term-doc.R: S(formula, attrs) (valued) > test-ergm-term-doc.R: Evaluation on an induced subgraph > test-ergm-term-doc.R: > test-ergm-term-doc.R: Sum(formulas, label) (valued) > test-ergm-term-doc.R: A sum (or an arbitrary linear combination) of one or more formulas > test-ergm-term-doc.R: > test-ergm-term-doc.R: Symmetrize(formula, rule="weak") (valued) > test-ergm-term-doc.R: Evaluation on symmetrized (undirected) network > test-ergm-term-doc.R: > test-ergm-term-doc.R: absdiff(attr, pow=1, form="sum") (valued) > test-ergm-term-doc.R: Absolute difference in nodal attribute > test-ergm-term-doc.R: > test-ergm-term-doc.R: absdiffcat(attr, base=NULL, levels=NULL, form="sum") (valued) > test-ergm-term-doc.R: Categorical absolute difference in nodal attribute > test-ergm-term-doc.R: > test-ergm-term-doc.R: atleast(threshold=0) (valued) > test-ergm-term-doc.R: Number of dyads with values greater than or equal to a threshold > test-ergm-term-doc.R: > test-ergm-term-doc.R: atmost(threshold=0) (valued) > test-ergm-term-doc.R: Number of dyads with values less than or equal to a threshold > test-ergm-term-doc.R: > test-ergm-term-doc.R: attrcov(attr, mat, form="sum") (valued) > test-ergm-term-doc.R: Edge covariate by attribute pairing > test-ergm-term-doc.R: > test-ergm-term-doc.R: b1cov(attr, form="sum") (valued) > test-ergm-term-doc.R: Main effect of a covariate for the first mode in a bipartite network > test-ergm-term-doc.R: > test-ergm-term-doc.R: b1factor(attr, base=1, levels=-1, form="sum") (valued) > test-ergm-term-doc.R: Factor attribute effect for the first mode in a bipartite network > test-ergm-term-doc.R: > test-ergm-term-doc.R: b1sociality(nodes=-1, form="sum") (valued) > test-ergm-term-doc.R: Degree > test-ergm-term-doc.R: > test-ergm-term-doc.R: b2cov(attr, form="sum") (valued) > test-ergm-term-doc.R: Main effect of a covariate for the second mode in a bipartite network > test-ergm-term-doc.R: > test-ergm-term-doc.R: b2factor(attr, base=1, levels=-1, form="sum") (valued) > test-ergm-term-doc.R: Factor attribute effect for the second mode in a bipartite network > test-ergm-term-doc.R: > test-ergm-term-doc.R: b2sociality(nodes=-1, form="sum") (valued) > test-ergm-term-doc.R: Degree > test-ergm-term-doc.R: > test-ergm-term-doc.R: cyclicalties(threshold=0) (valued) > test-ergm-term-doc.R: Cyclical ties > test-ergm-term-doc.R: > test-ergm-term-doc.R: cyclicalweights(twopath="min", combine="max", affect="min") (valued) > test-ergm-term-doc.R: Cyclical weights > test-ergm-term-doc.R: > test-ergm-term-doc.R: diff(attr, pow=1, dir="t-h", sign.action="identity", form ="sum") (valued) > test-ergm-term-doc.R: Difference > test-ergm-term-doc.R: > test-ergm-term-doc.R: edgecov(x, attrname=NULL, form="sum") (valued) > test-ergm-term-doc.R: Edge covariate > test-ergm-term-doc.R: > test-ergm-term-doc.R: edges (valued) > test-ergm-term-doc.R: nonzero (valued) > test-ergm-term-doc.R: Number of edges in the network > test-ergm-term-doc.R: > test-ergm-term-doc.R: equalto(value=0, tolerance=0) (valued) > test-ergm-term-doc.R: Number of dyads with values equal to a specific value (within tolerance) > test-ergm-term-doc.R: > test-ergm-term-doc.R: greaterthan(threshold=0) (valued) > test-ergm-term-doc.R: Number of dyads with values strictly greater than a threshold > test-ergm-term-doc.R: > test-ergm-term-doc.R: ininterval(lower=-Inf, upper=+Inf, open=c(TRUE,TRUE)) (valued) > test-ergm-term-doc.R: Number of dyads whose values are in an interval > test-ergm-term-doc.R: > test-ergm-term-doc.R: mm(attrs, levels=NULL, levels2=-1, form="sum") (valued) > test-ergm-term-doc.R: Mixing matrix cells and margins > test-ergm-term-doc.R: > test-ergm-term-doc.R: mutual(form="min",threshold=0) (valued) > test-ergm-term-doc.R: Mutuality > test-ergm-term-doc.R: > test-ergm-term-doc.R: nodecov(attr, form="sum") (valued) > test-ergm-term-doc.R: nodemain(attr, form="sum") (valued) > test-ergm-term-doc.R: Main effect of a covariate > test-ergm-term-doc.R: > test-ergm-term-doc.R: nodecovar(center, transform) (valued) > test-ergm-term-doc.R: Covariance of undirected dyad values incident on each actor > test-ergm-term-doc.R: > test-ergm-term-doc.R: nodefactor(attr, base=1, levels=-1, form="sum") (valued) > test-ergm-term-doc.R: Factor attribute effect > test-ergm-term-doc.R: > test-ergm-term-doc.R: nodeicov(attr, form="sum") (valued) > test-ergm-term-doc.R: Main effect of a covariate for in-edges > test-ergm-term-doc.R: > test-ergm-term-doc.R: nodeicovar(center, transform) (valued) > test-ergm-term-doc.R: Covariance of in-dyad values incident on each actor > test-ergm-term-doc.R: > test-ergm-term-doc.R: nodeifactor(attr, base=1, levels=-1, form="sum") (valued) > test-ergm-term-doc.R: Factor attribute effect for in-edges > test-ergm-term-doc.R: > test-ergm-term-doc.R: nodematch(attr, diff=FALSE, keep=NULL, levels=NULL, form="sum") (valued) > test-ergm-term-doc.R: match(attr, diff=FALSE, keep=NULL, levels=NULL, form="sum") (valued) > test-ergm-term-doc.R: Uniform homophily and differential homophily > test-ergm-term-doc.R: > test-ergm-term-doc.R: nodemix(attr, base=NULL, b1levels=NULL, b2levels=NULL, levels=NULL, levels2=-1, form="sum") (valued) > test-ergm-term-doc.R: Nodal attribute mixing > test-ergm-term-doc.R: > test-ergm-term-doc.R: nodeocov(attr, form="sum") (valued) > test-ergm-term-doc.R: Main effect of a covariate for out-edges > test-ergm-term-doc.R: > test-ergm-term-doc.R: nodeocovar(center, transform) (valued) > test-ergm-term-doc.R: Covariance of out-dyad values incident on each actor > test-ergm-term-doc.R: > test-ergm-term-doc.R: nodeofactor(attr, base=1, levels=-1, form="sum") (valued) > test-ergm-term-doc.R: Factor attribute effect for out-edges > test-ergm-term-doc.R: > test-ergm-term-doc.R: receiver(base=1, nodes=-1, form="sum") (valued) > test-ergm-term-doc.R: Receiver effect > test-ergm-term-doc.R: > test-ergm-term-doc.R: sender(base=1, nodes=-1, form="sum") (valued) > test-ergm-term-doc.R: Sender effect > test-ergm-term-doc.R: > test-ergm-term-doc.R: smallerthan(threshold=0) (valued) > test-ergm-term-doc.R: Number of dyads with values strictly smaller than a threshold > test-ergm-term-doc.R: > test-ergm-term-doc.R: sociality(attr=NULL, base=1, levels=NULL, nodes=-1, form="sum") (valued) > test-ergm-term-doc.R: Undirected degree > test-ergm-term-doc.R: > test-ergm-term-doc.R: sum(pow=1) (valued) > test-ergm-term-doc.R: Sum of dyad values (optionally taken to a power) > test-ergm-term-doc.R: > test-ergm-term-doc.R: transitiveweights(twopath="min", combine="max", affect="min") (valued) > test-ergm-term-doc.R: Transitive weights > test-ergm-term-doc.R: Found 4 matching ergm terms: > test-ergm-term-doc.R: Bernoulli > test-ergm-term-doc.R: Bernoulli reference > test-ergm-term-doc.R: > test-ergm-term-doc.R: DiscUnif(a,b) > test-ergm-term-doc.R: Discrete Uniform reference > test-ergm-term-doc.R: > test-ergm-term-doc.R: StdNormal > test-ergm-term-doc.R: Standard Normal reference > test-ergm-term-doc.R: > test-ergm-term-doc.R: Unif(a,b) > test-ergm-term-doc.R: Continuous Uniform reference > test-ergm-term-doc.R: Found 0 matching ergm terms: > test-ergm-term-doc.R: Found 1 matching ergm terms: > test-ergm-term-doc.R: Bernoulli > test-ergm-term-doc.R: Bernoulli reference > test-ergm-term-doc.R: Definitions for term(s) Bernoulli : > test-ergm-term-doc.R: Bernoulli > test-ergm-term-doc.R: Bernoulli reference: Specifies each > test-ergm-term-doc.R: dyad's baseline distribution to be Bernoulli with probability of > test-ergm-term-doc.R: the tie being 0.5 . This is the only reference measure used > test-ergm-term-doc.R: in binary mode. > test-ergm-term-doc.R: Keywords: binary, discrete, finite, nonnegative > test-ergm-term-doc.R: > test-ergm-term-doc.R: No terms named 'Cernoulli' were found. Try searching with search='Cernoulli'instead. > test-ergm-term-doc.R: Found > test-ergm-term-doc.R: 9 matching ergm terms: > test-ergm-term-doc.R: b1degrees > test-ergm-term-doc.R: Preserve the actor degree for bipartite networks > test-ergm-term-doc.R: > test-ergm-term-doc.R: b2degrees > test-ergm-term-doc.R: Preserve the receiver degree for bipartite networks > test-ergm-term-doc.R: > test-ergm-term-doc.R: bd(attribs, maxout, maxin, minout, minin) > test-ergm-term-doc.R: Constrain maximum and minimum vertex degree > test-ergm-term-doc.R: > test-ergm-term-doc.R: degreedist > test-ergm-term-doc.R: Preserve the degree distribution of the given network > test-ergm-term-doc.R: > test-ergm-term-doc.R: degrees > test-ergm-term-doc.R: nodedegrees > test-ergm-term-doc.R: Preserve the degree of each vertex of the given network > test-ergm-term-doc.R: > test-ergm-term-doc.R: idegreedist > test-ergm-term-doc.R: Preserve the indegree distribution > test-ergm-term-doc.R: > test-ergm-term-doc.R: idegrees > test-ergm-term-doc.R: Preserve indegree for directed networks > test-ergm-term-doc.R: > test-ergm-term-doc.R: odegreedist > test-ergm-term-doc.R: Preserve the outdegree distribution > test-ergm-term-doc.R: > test-ergm-term-doc.R: odegrees > test-ergm-term-doc.R: Preserve outdegree for directed networks > test-ergm-term-doc.R: Found > test-ergm-term-doc.R: > test-ergm-term-doc.R: 0 > test-ergm-term-doc.R: matching ergm terms: > test-ergm-term-doc.R: Found 16 matching ergm terms: > test-ergm-term-doc.R: Dyads(fix=NULL, vary=NULL) > test-ergm-term-doc.R: Constrain fixed or varying dyad-independent terms > test-ergm-term-doc.R: > test-ergm-term-doc.R: bd(attribs, maxout, maxin, minout, minin) > test-ergm-term-doc.R: Constrain maximum and minimum vertex degree > test-ergm-term-doc.R: > test-ergm-term-doc.R: blockdiag(attr) > test-ergm-term-doc.R: Block-diagonal structure constraint > test-ergm-term-doc.R: > test-ergm-term-doc.R: blocks(attr=NULL, levels=NULL, levels2=FALSE, b1levels=NULL, b2levels=NULL) > test-ergm-term-doc.R: Constrain blocks of dyads defined by mixing type on a vertex attribute. > test-ergm-term-doc.R: > test-ergm-term-doc.R: degreedist > test-ergm-term-doc.R: Preserve the degree distribution of the given network > test-ergm-term-doc.R: > test-ergm-term-doc.R: degrees > test-ergm-term-doc.R: nodedegrees > test-ergm-term-doc.R: Preserve the degree of each vertex of the given network > test-ergm-term-doc.R: > test-ergm-term-doc.R: dyadnoise(p01, p10) > test-ergm-term-doc.R: A soft constraint to adjust the sampled distribution for > test-ergm-term-doc.R: dyad-level noise with known perturbation probabilities > test-ergm-term-doc.R: > test-ergm-term-doc.R: egocentric(attr=NULL, direction="both") > test-ergm-term-doc.R: Preserve values of dyads incident on vertices with given attribute > test-ergm-term-doc.R: > test-ergm-term-doc.R: fixallbut(free.dyads) > test-ergm-term-doc.R: Preserve the dyad status in all but the given edges > test-ergm-term-doc.R: > test-ergm-term-doc.R: fixedas(fixed.dyads, present, absent) > test-ergm-term-doc.R: Fix specific dyads > test-ergm-term-doc.R: > test-ergm-term-doc.R: hamming > test-ergm-term-doc.R: Preserve the hamming distance to the given network (BROKEN: Do NOT Use) > test-ergm-term-doc.R: > test-ergm-term-doc.R: idegreedist > test-ergm-term-doc.R: Preserve the indegree distribution > test-ergm-term-doc.R: > test-ergm-term-doc.R: idegrees > test-ergm-term-doc.R: Preserve indegree for directed networks > test-ergm-term-doc.R: > test-ergm-term-doc.R: observed > test-ergm-term-doc.R: Preserve the observed dyads of the given network > test-ergm-term-doc.R: > test-ergm-term-doc.R: odegreedist > test-ergm-term-doc.R: Preserve the outdegree distribution > test-ergm-term-doc.R: > test-ergm-term-doc.R: odegrees > test-ergm-term-doc.R: Preserve outdegree for directed networks > test-ergm.bridge.llr.R: Setting up bridge sampling... > test-ergm-term-doc.R: Definitions for term(s) b1degrees : > test-ergm-term-doc.R: b1degrees > test-ergm-term-doc.R: Preserve the actor degree for bipartite networks: For bipartite networks, preserve the degree for the first mode of each vertex of the given > test-ergm-term-doc.R: network, while allowing the degree for the second mode to vary. > test-ergm-term-doc.R: Keywords: bipartite > test-ergm-term-doc.R: > test-ergm-term-doc.R: No terms named 'b3degrees' were found. Try searching with search='b3degrees'instead. > test-ergm-term-doc.R: Found > test-ergm.bridge.llr.R: Using 16 bridges: > test-ergm.bridge.llr.R: 1 > test-ergm-term-doc.R: 2 matching ergm proposals: > test-ergm-term-doc.R: CondB1Degree > test-ergm-term-doc.R: MHp for b1degree constraints > test-ergm-term-doc.R: > test-ergm-term-doc.R: CondB2Degree > test-ergm-term-doc.R: MHp for b2degree constraints > test-ergm-term-doc.R: Found 5 matching ergm proposals: > test-ergm-term-doc.R: ConstantEdges > test-ergm-term-doc.R: MHp for edges constraints > test-ergm-term-doc.R: > test-ergm-term-doc.R: DistRLE > test-ergm-term-doc.R: TODO > test-ergm-term-doc.R: > test-ergm-term-doc.R: SPDyad > test-ergm-term-doc.R: A proposal alternating between TNT and a triad-focused > test-ergm-term-doc.R: proposal > test-ergm-term-doc.R: > test-ergm-term-doc.R: TNT > test-ergm-term-doc.R: Default MH algorithm > test-ergm-term-doc.R: > test-ergm-term-doc.R: randomtoggle > test-ergm-term-doc.R: Propose a randomly selected dyad to toggle > test-ergm-term-doc.R: Found > test-ergm-term-doc.R: 0 matching ergm proposals: > test-ergm-term-doc.R: Found > test-ergm-term-doc.R: 18 matching ergm proposals: > test-ergm-term-doc.R: BDStratTNT > test-ergm-term-doc.R: TNT proposal with degree bounds, stratification, and a blocks constraint > test-ergm-term-doc.R: > test-ergm-term-doc.R: CondB1Degree > test-ergm-term-doc.R: MHp for b1degree constraints > test-ergm-term-doc.R: > test-ergm-term-doc.R: CondB2Degree > test-ergm-term-doc.R: MHp for b2degree constraints > test-ergm-term-doc.R: > test-ergm-term-doc.R: CondDegree > test-ergm-term-doc.R: MHp for degree constraints > test-ergm-term-doc.R: > test-ergm-term-doc.R: CondDegreeDist > test-ergm-term-doc.R: MHp for degreedist constraints > test-ergm-term-doc.R: > test-ergm-term-doc.R: CondDegreeMix > test-ergm-term-doc.R: MHp for degree mix constraints > test-ergm-term-doc.R: > test-ergm-term-doc.R: CondInDegree > test-ergm-term-doc.R: MHp for idegree constraints > test-ergm-term-doc.R: > test-ergm-term-doc.R: CondInDegreeDist > test-ergm-term-doc.R: MHp for idegreedist constraints > test-ergm-term-doc.R: > test-ergm-term-doc.R: CondOutDegree > test-ergm-term-doc.R: MHp for odegree constraints > test-ergm-term-doc.R: > test-ergm-term-doc.R: CondOutDegreeDist > test-ergm-term-doc.R: MHp for odegreedist constraints > test-ergm-term-doc.R: > test-ergm-term-doc.R: ConstantEdges > test-ergm-term-doc.R: MHp for edges constraints > test-ergm-term-doc.R: > test-ergm-term-doc.R: HammingConstantEdges > test-ergm-term-doc.R: TODO > test-ergm-term-doc.R: > test-ergm-term-doc.R: HammingTNT > test-ergm-term-doc.R: TODO > test-ergm-term-doc.R: > test-ergm-term-doc.R: SPDyad > test-ergm-term-doc.R: A proposal alternating between TNT and a triad-focused > test-ergm-term-doc.R: proposal > test-ergm-term-doc.R: > test-ergm-term-doc.R: TNT > test-ergm-term-doc.R: Default MH algorithm > test-ergm-term-doc.R: > test-ergm-term-doc.R: dyadnoise > test-ergm-term-doc.R: TODO > test-ergm-term-doc.R: > test-ergm-term-doc.R: dyadnoiseTNT > test-ergm-term-doc.R: TODO > test-ergm-term-doc.R: > test-ergm-term-doc.R: randomtoggle > test-ergm-term-doc.R: Propose a randomly selected dyad to toggle > test-ergm-term-doc.R: Definitions for proposal(s) randomtoggle : > test-ergm-term-doc.R: randomtoggle > test-ergm-term-doc.R: Propose a randomly selected dyad to toggle: Propose a randomly selected dyad to toggle > test-ergm-term-doc.R: Reference: Bernoulli Class: cross-sectional > test-ergm-term-doc.R: May Enforce: .dyads bd > test-ergm-term-doc.R: > test-ergm-term-doc.R: No proposals named 'mandomtoggle' were found. Try searching with search='mandomtoggle'instead. > test-ergm.bridge.llr.R: 2 > test-ergm-term-doc.R: > test-ergm-term-doc.R: 'ergm.count' 4.1.3 (2025-09-10), part of the Statnet Project > test-ergm-term-doc.R: * 'news(package="ergm.count")' for changes since last version > test-ergm-term-doc.R: * 'citation("ergm.count")' for citation information > test-ergm-term-doc.R: * 'https://statnet.org' for help, support, and other information > test-ergm-term-doc.R: > test-ergm.bridge.llr.R: 3 > test-ergm.bridge.llr.R: 4 > test-ergm.bridge.llr.R: 5 > test-ergm.bridge.llr.R: 6 > test-ergmMPLE.R: Starting maximum pseudolikelihood estimation (MPLE): > test-ergmMPLE.R: Obtaining the responsible dyads. > test-ergmMPLE.R: Evaluating the predictor and response matrix. > test-ergmMPLE.R: Maximizing the pseudolikelihood. > test-ergmMPLE.R: Finished MPLE. > test-ergmMPLE.R: Evaluating log-likelihood at the estimate. > test-ergmMPLE.R: > test-ergm.bridge.llr.R: 7 > test-ergm.bridge.llr.R: 8 > test-ergm.bridge.llr.R: 9 > test-ergm.bridge.llr.R: 10 > test-ergm.bridge.llr.R: 11 > test-ergm.bridge.llr.R: 12 > test-ergm.bridge.llr.R: 13 > test-ergm.bridge.llr.R: 14 > test-ergm.bridge.llr.R: 15 > test-ergm.bridge.llr.R: 16 > test-gflomiss.R: Starting maximum pseudolikelihood estimation (MPLE): > test-gflomiss.R: Obtaining the responsible dyads. > test-gflomiss.R: Evaluating the predictor and response matrix. > test-gflomiss.R: Maximizing the pseudolikelihood. > test-gflomiss.R: Finished MPLE. > test-gflomiss.R: Evaluating log-likelihood at the estimate. > test-gflomiss.R: > test-ergm.bridge.llr.R: . > test-ergm.bridge.llr.R: Setting up bridge sampling... > test-gflomiss.R: Starting maximum pseudolikelihood estimation (MPLE): > test-gflomiss.R: Obtaining the responsible dyads. > test-gflomiss.R: Evaluating the predictor and response matrix. > test-gflomiss.R: Maximizing the pseudolikelihood. > test-gflomiss.R: Finished MPLE. > test-gflomiss.R: Evaluating log-likelihood at the estimate. > test-gflomiss.R: > test-ergm.bridge.llr.R: Using 16 bridges: > test-ergm.bridge.llr.R: 1 > test-gflomiss.R: Starting maximum pseudolikelihood estimation (MPLE): > test-gflomiss.R: Obtaining the responsible dyads. > test-gflomiss.R: Evaluating the predictor and response matrix. > test-gflomiss.R: Maximizing the pseudolikelihood. > test-gflomiss.R: Finished MPLE. > test-gflomiss.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-gflomiss.R: Iteration 1 of at most 60: > test-ergm.bridge.llr.R: 2 > test-ergm.bridge.llr.R: 3 > test-ergm.bridge.llr.R: 4 > test-ergm.bridge.llr.R: 5 > test-ergm.bridge.llr.R: 6 > test-ergm.bridge.llr.R: 7 > test-ergm.bridge.llr.R: 8 > test-ergm.bridge.llr.R: 9 > test-ergm.bridge.llr.R: 10 > test-gflomiss.R: 1 > test-gflomiss.R: Optimizing with step length 1.0000. > test-ergm.bridge.llr.R: 11 > test-gflomiss.R: The log-likelihood improved by 0.0002. > test-gflomiss.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-gflomiss.R: Finished MCMLE. > test-ergm.bridge.llr.R: 12 > test-gflomiss.R: Evaluating log-likelihood at the estimate. > test-gflomiss.R: Fitting the dyad-independent submodel... > test-ergm.bridge.llr.R: 13 > test-gflomiss.R: Bridging between the dyad-independent submodel and the full model... > test-gflomiss.R: Setting up bridge sampling... > test-ergm.bridge.llr.R: 14 > test-gflomiss.R: Using 16 bridges: 1 > test-gflomiss.R: 2 > test-ergm.bridge.llr.R: 15 > test-gflomiss.R: 3 > test-gflomiss.R: 4 > test-ergm.bridge.llr.R: 16 > test-gflomiss.R: 5 > test-gflomiss.R: 6 > test-ergm.bridge.llr.R: . > test-gflomiss.R: 7 > test-ergm.bridge.llr.R: Fitting the dyad-independent submodel... > test-gflomiss.R: 8 > test-gflomiss.R: 9 > test-ergm.bridge.llr.R: Bridging between the dyad-independent submodel and the full model... > test-ergm.bridge.llr.R: Setting up bridge sampling... > test-gflomiss.R: 10 > test-gflomiss.R: 11 > test-ergm.bridge.llr.R: Using 16 bridges: > test-ergm.bridge.llr.R: 1 > test-gflomiss.R: 12 > test-gflomiss.R: 13 > test-ergm.bridge.llr.R: 2 > test-gflomiss.R: 14 > test-gflomiss.R: 15 > test-ergm.bridge.llr.R: 3 > test-gflomiss.R: 16 > test-ergm.bridge.llr.R: 4 > test-gflomiss.R: . > test-gflomiss.R: Bridging finished. > test-gflomiss.R: > test-gflomiss.R: This model was fit using MCMC. To examine model diagnostics and check > test-gflomiss.R: for degeneracy, use the mcmc.diagnostics() function. > test-ergm.bridge.llr.R: 5 > test-gflomiss.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-gflomiss.R: Iteration 1 of at most 60: > test-ergm.bridge.llr.R: 6 > test-ergm.bridge.llr.R: 7 > test-ergm.bridge.llr.R: 8 > test-ergm.bridge.llr.R: 9 > test-ergm.bridge.llr.R: 10 > test-ergm.bridge.llr.R: 11 > test-gflomiss.R: 1 > test-gflomiss.R: Optimizing with step length 1.0000. > test-gflomiss.R: The log-likelihood improved by 0.0241. > test-ergm.bridge.llr.R: 12 > test-gflomiss.R: Convergence test p-value: 0.0001. Converged with 99% confidence. > test-gflomiss.R: Finished MCMLE. > test-gflomiss.R: Evaluating log-likelihood at the estimate. > test-gflomiss.R: Fitting the dyad-independent submodel... > test-ergm.bridge.llr.R: 13 > test-ergm.bridge.llr.R: 14 > test-gflomiss.R: Bridging between the dyad-independent submodel and the full model... > test-gflomiss.R: Setting up bridge sampling... > test-ergm.bridge.llr.R: 15 > test-gflomiss.R: Using 16 bridges: 1 > test-ergm.bridge.llr.R: 16 > test-gflomiss.R: 2 > test-gflomiss.R: 3 > test-gflomiss.R: 4 > test-ergm.bridge.llr.R: . > test-ergm.bridge.llr.R: Bridging finished. > test-gflomiss.R: 5 > test-ergm.bridge.llr.R: Setting up bridge sampling... > test-gflomiss.R: 6 > test-gflomiss.R: 7 > test-gflomiss.R: 8 > test-gflomiss.R: 9 > test-ergm.bridge.llr.R: Using 16 bridges: 1 > test-gflomiss.R: 10 > test-gflomiss.R: 11 > test-gflomiss.R: 12 > test-gflomiss.R: 13 > test-gflomiss.R: 14 > test-gflomiss.R: 15 > test-gflomiss.R: 16 > test-ergm.bridge.llr.R: 2 > test-gflomiss.R: . > test-gflomiss.R: Bridging finished. > test-gflomiss.R: > test-gflomiss.R: This model was fit using MCMC. To examine model diagnostics and check > test-gflomiss.R: for degeneracy, use the mcmc.diagnostics() function. > test-ergm.bridge.llr.R: 3 > test-gmonkmiss.R: odegree3 odegree4 odegree5 odegree6 > test-gmonkmiss.R: 1 5 7 5 > test-gmonkmiss.R: idegree2 idegree3 idegree4 idegree5 idegree6 idegree7 idegree8 idegree10 > test-gmonkmiss.R: 3 5 1 3 2 1 1 1 > test-gmonkmiss.R: idegree11 > test-gmonkmiss.R: 1 > test-gmonkmiss.R: Starting maximum pseudolikelihood estimation (MPLE): > test-gmonkmiss.R: Obtaining the responsible dyads. > test-gmonkmiss.R: Evaluating the predictor and response matrix. > test-gmonkmiss.R: Maximizing the pseudolikelihood. > test-gmonkmiss.R: Finished MPLE. > test-ergm.bridge.llr.R: 4 > test-gmonkmiss.R: Starting maximum pseudolikelihood estimation (MPLE): > test-gmonkmiss.R: Obtaining the responsible dyads. > test-gmonkmiss.R: Evaluating the predictor and response matrix. > test-gmonkmiss.R: Maximizing the pseudolikelihood. > test-gmonkmiss.R: Finished MPLE. > test-gmonkmiss.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-gmonkmiss.R: Iteration 1 of at most 3: > test-ergm.bridge.llr.R: 5 > test-ergm.bridge.llr.R: 6 > test-ergm.bridge.llr.R: 7 > test-ergm.bridge.llr.R: 8 > test-gmonkmiss.R: 1 Optimizing with step length 1.0000. > test-gmonkmiss.R: The log-likelihood improved by 0.6245. > test-gmonkmiss.R: Estimating equations are not within tolerance region. > test-gmonkmiss.R: Iteration 2 of at most 3: > test-ergm.bridge.llr.R: 9 > test-ergm.bridge.llr.R: 10 > test-gmonkmiss.R: 1 Optimizing with step length 1.0000. > test-ergm.bridge.llr.R: 11 > test-gmonkmiss.R: The log-likelihood improved by 0.0078. > test-gmonkmiss.R: Convergence test p-value: 0.0005. Converged with 99% confidence. > test-gmonkmiss.R: Finished MCMLE. > test-gmonkmiss.R: This model was fit using MCMC. To examine model diagnostics and check > test-gmonkmiss.R: for degeneracy, use the mcmc.diagnostics() function. > test-gmonkmiss.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'. > test-gmonkmiss.R: Model and/or observational constraints are not dyad-independent. Dyad imputation cannot be used. Please ensure your LHS network satisfies all constraints. > test-gmonkmiss.R: Starting contrastive divergence estimation via CD-MCMLE: > test-gmonkmiss.R: Iteration 1 of at most 60: > test-gmonkmiss.R: Convergence test P-value:3.3e-34 > test-gmonkmiss.R: 1 > test-gmonkmiss.R: The log-likelihood improved by 0.4205. > test-gmonkmiss.R: Iteration 2 of at most 60: > test-gmonkmiss.R: Convergence test P-value:1.8e-14 > test-ergm.bridge.llr.R: 12 > test-gmonkmiss.R: 1 > test-gmonkmiss.R: The log-likelihood improved by 0.1501. > test-gmonkmiss.R: Iteration 3 of at most 60: > test-gmonkmiss.R: Convergence test P-value:2e-04 > test-gmonkmiss.R: 1 > test-gmonkmiss.R: The log-likelihood improved by 0.03536. > test-gmonkmiss.R: Iteration 4 of at most 60: > test-gmonkmiss.R: Convergence test P-value:1.6e-01 > test-gmonkmiss.R: 1 > test-gmonkmiss.R: The log-likelihood improved by 0.007343. > test-gmonkmiss.R: Iteration 5 of at most 60: > test-gmonkmiss.R: Convergence test P-value:2.4e-01 > test-gmonkmiss.R: 1 > test-gmonkmiss.R: The log-likelihood improved by 0.00569. > test-gmonkmiss.R: Iteration 6 of at most 60: > test-gmonkmiss.R: Convergence test P-value:9.9e-02 > test-gmonkmiss.R: 1 > test-gmonkmiss.R: The log-likelihood improved by 0.00904. > test-gmonkmiss.R: Iteration 7 of at most 60: > test-gmonkmiss.R: Convergence test P-value:7.6e-01 > test-gmonkmiss.R: Convergence detected. Stopping. > test-gmonkmiss.R: 1 > test-gmonkmiss.R: The log-likelihood improved by 0.001102. > test-gmonkmiss.R: Finished CD. > test-gmonkmiss.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-gmonkmiss.R: Iteration 1 of at most 3: > test-ergm.bridge.llr.R: 13 > test-ergm.bridge.llr.R: 14 > test-ergm.bridge.llr.R: 15 > test-ergm.bridge.llr.R: 16 > test-gmonkmiss.R: 1 Optimizing with step length 1.0000. > test-gmonkmiss.R: The log-likelihood improved by 0.4514. > test-gmonkmiss.R: Estimating equations are not within tolerance region. > test-gmonkmiss.R: Iteration 2 of at most 3: > test-ergm.bridge.llr.R: . > test-ergm.bridge.llr.R: Setting up bridge sampling... > test-ergm.bridge.llr.R: Using 16 bridges: 1 > test-ergm.bridge.llr.R: 2 > test-ergm.bridge.llr.R: 3 > test-ergm.bridge.llr.R: 4 > test-gmonkmiss.R: 1 > test-gmonkmiss.R: Optimizing with step length 1.0000. > test-gmonkmiss.R: The log-likelihood improved by 0.0105. > test-gmonkmiss.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-gmonkmiss.R: Finished MCMLE. > test-gmonkmiss.R: This model was fit using MCMC. To examine model diagnostics and check > test-gmonkmiss.R: for degeneracy, use the mcmc.diagnostics() function. > test-ergm.bridge.llr.R: 5 > test-gmonkmiss.R: Starting maximum pseudolikelihood estimation (MPLE): > test-gmonkmiss.R: Obtaining the responsible dyads. > test-gmonkmiss.R: Evaluating the predictor and response matrix. > test-gmonkmiss.R: Maximizing the pseudolikelihood. > test-gmonkmiss.R: Finished MPLE. > test-ergm.bridge.llr.R: 6 > test-gmonkmiss.R: Starting maximum pseudolikelihood estimation (MPLE): > test-gmonkmiss.R: Obtaining the responsible dyads. > test-gmonkmiss.R: Evaluating the predictor and response matrix. > test-gmonkmiss.R: Maximizing the pseudolikelihood. > test-gmonkmiss.R: Finished MPLE. > test-gmonkmiss.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-gmonkmiss.R: Iteration 1 of at most 3: > test-ergm.bridge.llr.R: 7 > test-ergm.bridge.llr.R: 8 > test-ergm.bridge.llr.R: 9 > test-ergm.bridge.llr.R: 10 > test-gmonkmiss.R: 1 > test-gmonkmiss.R: Optimizing with step length 1.0000. > test-gmonkmiss.R: The log-likelihood improved by 0.7035. > test-gmonkmiss.R: Estimating equations are not within tolerance region. > test-gmonkmiss.R: Iteration 2 of at most 3: > test-ergm.bridge.llr.R: 11 > test-ergm.bridge.llr.R: 12 > test-ergm.bridge.llr.R: 13 > test-gmonkmiss.R: 1 > test-gmonkmiss.R: Optimizing with step length 1.0000. > test-gmonkmiss.R: The log-likelihood improved by 0.0078. > test-gmonkmiss.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-gmonkmiss.R: Finished MCMLE. > test-ergm.bridge.llr.R: 14 > test-gmonkmiss.R: This model was fit using MCMC. To examine model diagnostics and check > test-gmonkmiss.R: for degeneracy, use the mcmc.diagnostics() function. > test-ergm.bridge.llr.R: 15 > test-gof.R: Starting maximum pseudolikelihood estimation (MPLE): > test-gof.R: Obtaining the responsible dyads. > test-gof.R: Evaluating the predictor and response matrix. > test-gof.R: Maximizing the pseudolikelihood. > test-gof.R: Finished MPLE. > test-gof.R: Evaluating log-likelihood at the estimate. > test-gof.R: > test-ergm.bridge.llr.R: 16 > test-ergm.bridge.llr.R: . > test-ergm.bridge.llr.R: Fitting the dyad-independent submodel... > test-gof.R: Starting maximum pseudolikelihood estimation (MPLE): > test-gof.R: Obtaining the responsible dyads. > test-gof.R: Evaluating the predictor and response matrix. > test-gof.R: Maximizing the pseudolikelihood. > test-gof.R: Finished MPLE. > test-gof.R: Evaluating log-likelihood at the estimate. > test-gof.R: > test-ergm.bridge.llr.R: Bridging between the dyad-independent submodel and the full model... > test-ergm.bridge.llr.R: Setting up bridge sampling... > test-ergm.bridge.llr.R: Using 16 bridges: > test-ergm.bridge.llr.R: 1 > test-gof.R: Starting maximum pseudolikelihood estimation (MPLE): > test-gof.R: Obtaining the responsible dyads. > test-gof.R: Evaluating the predictor and response matrix. > test-ergm.bridge.llr.R: 2 > test-gof.R: Maximizing the pseudolikelihood. > test-gof.R: Finished MPLE. > test-gof.R: Evaluating log-likelihood at the estimate. > test-gof.R: > test-ergm.bridge.llr.R: 3 > test-ergm.bridge.llr.R: 4 > test-ergm.bridge.llr.R: 5 > test-ergm.bridge.llr.R: 6 > test-ergm.bridge.llr.R: 7 > test-ergm.bridge.llr.R: 8 > test-ergm.bridge.llr.R: 9 > test-ergm.bridge.llr.R: 10 > test-ergm.bridge.llr.R: 11 > test-ergm.bridge.llr.R: 12 > test-ergm.bridge.llr.R: 13 > test-ergm.bridge.llr.R: 14 > test-ergm.bridge.llr.R: 15 > test-ergm.bridge.llr.R: 16 > test-ergm.bridge.llr.R: . > test-ergm.bridge.llr.R: Bridging finished. > test-metrics.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-metrics.R: Iteration 1 of at most 60: > test-miss-dep.R: Best valid proposal 'ConstantEdges' cannot take into account hint(s) 'triadic'. > test-miss-dep.R: Model and/or observational constraints are not dyad-independent. Dyad imputation cannot be used. Please ensure your LHS network satisfies all constraints. > test-miss-dep.R: Starting contrastive divergence estimation via CD-MCMLE: > test-miss-dep.R: Iteration 1 of at most 60: > test-miss-dep.R: Convergence test P-value:4.6e-47 > test-miss-dep.R: 1 > test-miss-dep.R: The log-likelihood improved by 1.824. > test-miss-dep.R: Iteration 2 of at most 60: > test-miss-dep.R: Convergence test P-value:1.5e-23 > test-miss-dep.R: 1 > test-miss-dep.R: The log-likelihood improved by 0.6054. > test-miss-dep.R: Iteration 3 of at most 60: > test-metrics.R: 1 > test-metrics.R: Optimizing with step length 0.4613. > test-metrics.R: The log-likelihood improved by 4.1429. > test-metrics.R: Estimating equations are not within tolerance region. > test-metrics.R: Iteration 2 of at most 60: > test-miss-dep.R: Convergence test P-value:1.1e-07 > test-miss-dep.R: 1 > test-miss-dep.R: The log-likelihood improved by 0.1283. > test-miss-dep.R: Iteration 4 of at most 60: > test-miss-dep.R: Convergence test P-value:3.3e-04 > test-miss-dep.R: 1 > test-miss-dep.R: The log-likelihood improved by 0.05435. > test-miss-dep.R: Iteration 5 of at most 60: > test-miss-dep.R: Convergence test P-value:2.1e-01 > test-miss-dep.R: 1 > test-miss-dep.R: The log-likelihood improved by 0.006185. > test-miss-dep.R: Iteration 6 of at most 60: > test-metrics.R: 1 > test-metrics.R: Optimizing with step length 0.8364. > test-metrics.R: The log-likelihood improved by 4.7215. > test-metrics.R: Estimating equations are not within tolerance region. > test-metrics.R: Iteration 3 of at most 60: > test-miss-dep.R: Convergence test P-value:4.1e-01 > test-miss-dep.R: 1 > test-miss-dep.R: The log-likelihood improved by 0.002664. > test-miss-dep.R: Iteration 7 of at most 60: > test-metrics.R: 1 > test-metrics.R: Optimizing with step length 1.0000. > test-metrics.R: The log-likelihood improved by 1.1346. > test-metrics.R: Estimating equations are not within tolerance region. > test-metrics.R: Iteration 4 of at most 60: > test-miss-dep.R: Convergence test P-value:1.6e-01 > test-miss-dep.R: 1 > test-miss-dep.R: The log-likelihood improved by 0.007694. > test-miss-dep.R: Iteration 8 of at most 60: > test-metrics.R: 1 Optimizing with step length 1.0000. > test-metrics.R: The log-likelihood improved by 0.1129. > test-metrics.R: Estimating equations are not within tolerance region. > test-metrics.R: Iteration 5 of at most 60: > test-miss-dep.R: Convergence test P-value:1.9e-01 > test-miss-dep.R: 1 > test-miss-dep.R: The log-likelihood improved by 0.006878. > test-miss-dep.R: Iteration 9 of at most 60: > test-miss-dep.R: Convergence test P-value:7.8e-01 > test-miss-dep.R: Convergence detected. Stopping. > test-miss-dep.R: 1 > test-miss-dep.R: The log-likelihood improved by 0.0003111. > test-miss-dep.R: Finished CD. > test-miss-dep.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-miss-dep.R: Iteration 1 of at most 60: > test-metrics.R: 1 > test-metrics.R: Optimizing with step length 1.0000. > test-metrics.R: The log-likelihood improved by 0.0037. > test-metrics.R: Convergence test p-value: 0.0004. Converged with 99% confidence. > test-metrics.R: Finished MCMLE. > test-metrics.R: This model was fit using MCMC. To examine model diagnostics and check > test-metrics.R: for degeneracy, use the mcmc.diagnostics() function. > test-metrics.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-metrics.R: Iteration 1 of at most 60: > test-miss-dep.R: Post-burnin sample is constant; returning. > test-miss-dep.R: 1 > test-miss-dep.R: Optimizing with step length 1.0000. > test-metrics.R: 1 > test-miss-dep.R: The log-likelihood improved by 0.0017. > test-metrics.R: Optimizing with step length 0.4018. > test-metrics.R: The log-likelihood improved by 3.2780. > test-metrics.R: Estimating equations are not within tolerance region. > test-metrics.R: Iteration 2 of at most 60: > test-miss-dep.R: Convergence test p-value: < 0.0001. > test-miss-dep.R: Converged with 99% confidence. > test-miss-dep.R: Finished MCMLE. > test-miss-dep.R: Evaluating log-likelihood at the estimate. > test-miss-dep.R: Setting up bridge sampling... > test-miss-dep.R: Using 16 bridges: 1 > test-miss-dep.R: 2 > test-metrics.R: 1 > test-metrics.R: Optimizing with step length 0.6020. > test-metrics.R: The log-likelihood improved by 3.4584. > test-metrics.R: Estimating equations are not within tolerance region. > test-metrics.R: Iteration 3 of at most 60: > test-miss-dep.R: 3 > test-miss-dep.R: 4 > test-miss-dep.R: 5 > test-miss-dep.R: 6 > test-metrics.R: 1 > test-metrics.R: Optimizing with step length 1.0000. > test-metrics.R: The log-likelihood improved by 2.2132. > test-metrics.R: Estimating equations are not within tolerance region. > test-metrics.R: Iteration 4 of at most 60: > test-miss-dep.R: 7 > test-miss-dep.R: 8 > test-miss-dep.R: 9 > test-miss-dep.R: 10 > test-miss-dep.R: 11 > test-metrics.R: 1 > test-metrics.R: Optimizing with step length 1.0000. > test-metrics.R: The log-likelihood improved by 0.0377. > test-metrics.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-metrics.R: Finished MCMLE. > test-metrics.R: This model was fit using MCMC. To examine model diagnostics and check > test-metrics.R: for degeneracy, use the mcmc.diagnostics() function. > test-miss-dep.R: 12 > test-miss-dep.R: 13 > test-metrics.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-metrics.R: Iteration 1 of at most 60: > test-miss-dep.R: 14 > test-miss-dep.R: 15 > test-miss-dep.R: 16 > test-miss-dep.R: . > test-miss-dep.R: Note: The constraint on the sample space is not dyad-independent. Null > test-miss-dep.R: model likelihood is only implemented for dyad-independent constraints > test-miss-dep.R: at this time. Number of observations is similarly poorly defined. This > test-miss-dep.R: means that all likelihood-based inference (LRT, Analysis of Deviance, > test-miss-dep.R: AIC, BIC, etc.) is only valid between models with the same reference > test-miss-dep.R: distribution and constraints. > test-miss-dep.R: > test-miss-dep.R: This model was fit using MCMC. To examine model diagnostics and check > test-miss-dep.R: for degeneracy, use the mcmc.diagnostics() function. > test-miss.CD.R: n= > test-miss.CD.R: 20 > test-miss.CD.R: , density=0.1, missing=0.1 > test-metrics.R: 1 > test-metrics.R: Optimizing with step length 0.4158. > test-metrics.R: The log-likelihood improved by 1.9115. > test-metrics.R: Estimating equations are not within tolerance region. > test-metrics.R: Iteration 2 of at most 60: > test-metrics.R: 1 > test-metrics.R: Optimizing with step length 0.4804. > test-miss.CD.R: Starting contrastive divergence estimation via CD-MCMLE: > test-miss.CD.R: Iteration 1 of at most 60: > test-metrics.R: The log-likelihood improved by 2.5519. > test-metrics.R: Estimating equations are not within tolerance region. > test-metrics.R: Iteration 3 of at most 60: > test-miss.CD.R: Convergence test P-value:3e-13 > test-miss.CD.R: 1 > test-miss.CD.R: The log-likelihood improved by 0.2096. > test-miss.CD.R: Iteration 2 of at most 60: > test-miss.CD.R: Convergence test P-value:1.4e-12 > test-miss.CD.R: 1 > test-miss.CD.R: The log-likelihood improved by 0.1974. > test-miss.CD.R: Iteration 3 of at most 60: > test-metrics.R: 1 > test-metrics.R: Optimizing with step length 1.0000. > test-miss.CD.R: Convergence test P-value:5.8e-11 > test-miss.CD.R: 1 > test-metrics.R: The log-likelihood improved by 2.9415. > test-metrics.R: Estimating equations are not within tolerance region. > test-metrics.R: Iteration 4 of at most 60: > test-miss.CD.R: The log-likelihood improved by 0.341. > test-miss.CD.R: Iteration 4 of at most 60: > test-miss.CD.R: Convergence test P-value:4e-15 > test-miss.CD.R: 1 > test-miss.CD.R: The log-likelihood improved by 0.1744. > test-miss.CD.R: Iteration 5 of at most 60: > test-miss.CD.R: Convergence test P-value:1.3e-16 > test-miss.CD.R: 1 > test-miss.CD.R: The log-likelihood improved by 0.6862. > test-miss.CD.R: Iteration 6 of at most 60: > test-miss.CD.R: Convergence test P-value:8.3e-15 > test-miss.CD.R: 1 > test-miss.CD.R: The log-likelihood improved by 0.3025. > test-miss.CD.R: Iteration 7 of at most 60: > test-miss.CD.R: Convergence test P-value:1.5e-19 > test-miss.CD.R: 1 > test-miss.CD.R: The log-likelihood improved by 0.1888. > test-miss.CD.R: Iteration 8 of at most 60: > test-miss.CD.R: Convergence test P-value:2.3e-17 > test-miss.CD.R: 1 > test-miss.CD.R: The log-likelihood improved by 0.2862. > test-miss.CD.R: Iteration 9 of at most 60: > test-miss.CD.R: Convergence test P-value:1.6e-07 > test-miss.CD.R: 1 > test-miss.CD.R: The log-likelihood improved by 0.2007. > test-miss.CD.R: Iteration 10 of at most 60: > test-metrics.R: 1 > test-metrics.R: Optimizing with step length 1.0000. > test-metrics.R: The log-likelihood improved by 0.1226. > test-miss.CD.R: Convergence test P-value:2.1e-02 > test-miss.CD.R: 1 > test-miss.CD.R: The log-likelihood improved by 0.03854. > test-miss.CD.R: Iteration 11 of at most 60: > test-metrics.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-metrics.R: Finished MCMLE. > test-metrics.R: This model was fit using MCMC. To examine model diagnostics and check > test-metrics.R: for degeneracy, use the mcmc.diagnostics() function. > test-miss.CD.R: Convergence test P-value:7.9e-05 > test-miss.CD.R: 1 > test-miss.CD.R: The log-likelihood improved by 0.1259. > test-miss.CD.R: Iteration 12 of at most 60: > test-miss.CD.R: Convergence test P-value:7.9e-01 > test-miss.CD.R: Convergence detected. Stopping. > test-miss.CD.R: 1 > test-miss.CD.R: The log-likelihood improved by 0.0004877. > test-miss.CD.R: Finished CD. > test-miss.CD.R: This model was fit using MCMC. To examine model diagnostics and check > test-miss.CD.R: for degeneracy, use the mcmc.diagnostics() function. > test-metrics.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-metrics.R: Iteration 1 of at most 60: > test-miss.CD.R: Starting contrastive divergence estimation via CD-MCMLE: > test-miss.CD.R: Iteration 1 of at most 60: > test-miss.CD.R: Convergence test P-value:9.5e-68 > test-miss.CD.R: 1 > test-miss.CD.R: The log-likelihood improved by 0.7099. > test-miss.CD.R: Iteration 2 of at most 60: > test-miss.CD.R: Convergence test P-value:1.6e-64 > test-miss.CD.R: 1 > test-miss.CD.R: The log-likelihood improved by 0.5925. > test-miss.CD.R: Iteration 3 of at most 60: > test-miss.CD.R: Convergence test P-value:9.6e-53 > test-miss.CD.R: 1 > test-miss.CD.R: The log-likelihood improved by 0.6327. > test-miss.CD.R: Iteration 4 of at most 60: > test-miss.CD.R: Convergence test P-value:1.1e-37 > test-miss.CD.R: 1 > test-miss.CD.R: The log-likelihood improved by 0.8025. > test-miss.CD.R: Iteration 5 of at most 60: > test-miss.CD.R: Convergence test P-value:1.4e-30 > test-miss.CD.R: 1 > test-miss.CD.R: The log-likelihood improved by 0.583. > test-miss.CD.R: Iteration 6 of at most 60: > test-miss.CD.R: Convergence test P-value:8.6e-19 > test-miss.CD.R: 1 > test-miss.CD.R: The log-likelihood improved by 0.6591. > test-miss.CD.R: Iteration 7 of at most 60: > test-miss.CD.R: Convergence test P-value:6.2e-04 > test-miss.CD.R: 1 > test-miss.CD.R: The log-likelihood improved by 0.05663. > test-miss.CD.R: Iteration 8 of at most 60: > test-metrics.R: 1 > test-metrics.R: 2 > test-metrics.R: 3 > test-metrics.R: 4 > test-metrics.R: 5 6 > test-metrics.R: 7 > test-metrics.R: 8 > test-metrics.R: 9 10 > test-metrics.R: 11 > test-metrics.R: Optimizing with step length 0.3934. > test-metrics.R: The log-likelihood improved by 4.1457. > test-metrics.R: Estimating equations are not within tolerance region. > test-metrics.R: Iteration 2 of at most 60: > test-miss.CD.R: Convergence test P-value:2.4e-01 > test-miss.CD.R: 1 > test-miss.CD.R: The log-likelihood improved by 0.007268. > test-miss.CD.R: Iteration 9 of at most 60: > test-miss.CD.R: Convergence test P-value:9.1e-01 > test-miss.CD.R: Convergence detected. Stopping. > test-miss.CD.R: 1 > test-miss.CD.R: The log-likelihood improved by < 0.0001. > test-miss.CD.R: Finished CD. > test-miss.CD.R: This model was fit using MCMC. To examine model diagnostics and check > test-miss.CD.R: for degeneracy, use the mcmc.diagnostics() function. > test-metrics.R: 1 > test-metrics.R: Optimizing with step length 1.0000. > test-metrics.R: The log-likelihood improved by 2.8651. > test-metrics.R: Estimating equations are not within tolerance region. > test-metrics.R: Iteration 3 of at most 60: > test-miss.CD.R: Starting contrastive divergence estimation via CD-MCMLE: > test-miss.CD.R: Iteration 1 of at most 60: > test-miss.CD.R: Convergence test P-value:1.3e-54 > test-miss.CD.R: 1 > test-miss.CD.R: The log-likelihood improved by 0.5854. > test-miss.CD.R: Iteration 2 of at most 60: > test-miss.CD.R: Convergence test P-value:4.4e-52 > test-miss.CD.R: 1 > test-miss.CD.R: The log-likelihood improved by 0.6164. > test-miss.CD.R: Iteration 3 of at most 60: > test-miss.CD.R: Convergence test P-value:4.5e-46 > test-miss.CD.R: 1 > test-miss.CD.R: The log-likelihood improved by 0.6486. > test-miss.CD.R: Iteration 4 of at most 60: > test-miss.CD.R: Convergence test P-value:4.6e-32 > test-miss.CD.R: 1 > test-miss.CD.R: The log-likelihood improved by 1.361. > test-miss.CD.R: Iteration 5 of at most 60: > test-miss.CD.R: Convergence test P-value:6.8e-14 > test-miss.CD.R: 1 > test-miss.CD.R: The log-likelihood improved by 0.379. > test-miss.CD.R: Iteration 6 of at most 60: > test-miss.CD.R: Convergence test P-value:4.6e-03 > test-miss.CD.R: 1 > test-miss.CD.R: The log-likelihood improved by 0.05118. > test-miss.CD.R: Iteration 7 of at most 60: > test-miss.CD.R: Convergence test P-value:1.9e-02 > test-miss.CD.R: 1 > test-miss.CD.R: The log-likelihood improved by 0.03478. > test-miss.CD.R: Iteration 8 of at most 60: > test-miss.CD.R: Convergence test P-value:6.5e-01 > test-miss.CD.R: Convergence detected. Stopping. > test-miss.CD.R: 1 > test-miss.CD.R: The log-likelihood improved by 0.001186. > test-miss.CD.R: Finished CD. > test-miss.CD.R: This model was fit using MCMC. To examine model diagnostics and check > test-miss.CD.R: for degeneracy, use the mcmc.diagnostics() function. > test-metrics.R: 1 Optimizing with step length 1.0000. > test-metrics.R: The log-likelihood improved by 0.3360. > test-metrics.R: Estimating equations are not within tolerance region. > test-metrics.R: Iteration 4 of at most 60: > test-miss.CD.R: Network statistics: > test-miss.CD.R: edges > test-miss.CD.R: esp#1 esp#2 esp#3 esp#4 esp#5 esp#6 esp#7 esp#8 esp#9 esp#10 > test-miss.CD.R: 50 24 3 0 0 0 0 0 0 0 0 > test-miss.CD.R: esp#11 esp#12 esp#13 esp#14 esp#15 esp#16 esp#17 esp#18 esp#19 esp#20 esp#21 > test-miss.CD.R: 0 0 0 0 0 0 0 0 0 0 0 > test-miss.CD.R: esp#22 esp#23 esp#24 esp#25 esp#26 esp#27 esp#28 > test-miss.CD.R: 0 0 0 0 0 0 0 > test-miss.CD.R: Correct estimate = > test-miss.CD.R: -2.028148 > test-miss.CD.R: Starting contrastive divergence estimation via CD-MCMLE: > test-miss.CD.R: Iteration 1 of at most 60: > test-miss.CD.R: Convergence test P-value:1.8e-283 > test-miss.CD.R: 1 > test-miss.CD.R: 2 > test-metrics.R: 1 > test-metrics.R: Optimizing with step length 1.0000. > test-miss.CD.R: The log-likelihood improved by 1.712. > test-miss.CD.R: Iteration 2 of at most 60: > test-metrics.R: The log-likelihood improved by 0.0827. > test-metrics.R: Convergence test p-value: 0.0004. > test-metrics.R: Converged with 99% confidence. > test-metrics.R: Finished MCMLE. > test-metrics.R: This model was fit using MCMC. To examine model diagnostics and check > test-metrics.R: for degeneracy, use the mcmc.diagnostics() function. > test-miss.CD.R: Convergence test P-value:1.8e-235 > test-miss.CD.R: 1 > test-miss.CD.R: 2 > test-miss.CD.R: The log-likelihood improved by 1.493. > test-miss.CD.R: Iteration 3 of at most 60: > test-metrics.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-metrics.R: Iteration 1 of at most 60: > test-miss.CD.R: Convergence test P-value:9.3e-184 > test-miss.CD.R: 1 > test-miss.CD.R: 2 > test-miss.CD.R: The log-likelihood improved by 1.167. > test-miss.CD.R: Iteration 4 of at most 60: > test-miss.CD.R: Convergence test P-value:8.9e-150 > test-miss.CD.R: 1 > test-miss.CD.R: 2 > test-miss.CD.R: The log-likelihood improved by < 0.0001. > test-miss.CD.R: Iteration 5 of at most 60: > test-miss.CD.R: Convergence test P-value:1.3e-156 > test-miss.CD.R: 1 > test-miss.CD.R: 2 > test-metrics.R: 1 > test-metrics.R: 2 > test-metrics.R: 3 > test-metrics.R: 4 5 > test-metrics.R: 6 > test-metrics.R: 7 > test-metrics.R: 8 9 > test-metrics.R: 10 > test-metrics.R: 11 > test-metrics.R: Optimizing with step length 0.3701. > test-miss.CD.R: The log-likelihood improved by < 0.0001. > test-miss.CD.R: Iteration 6 of at most 60: > test-metrics.R: The log-likelihood improved by 2.3554. > test-metrics.R: Estimating equations are not within tolerance region. > test-metrics.R: Iteration 2 of at most 60: > test-miss.CD.R: Convergence test P-value:1.5e-147 > test-miss.CD.R: 1 > test-miss.CD.R: 2 > test-miss.CD.R: The log-likelihood improved by < 0.0001. > test-miss.CD.R: Iteration 7 of at most 60: > test-miss.CD.R: Convergence test P-value:2.2e-155 > test-miss.CD.R: 1 > test-miss.CD.R: 2 > test-miss.CD.R: The log-likelihood improved by < 0.0001. > test-miss.CD.R: Iteration 8 of at most 60: > test-metrics.R: 1 > test-metrics.R: 2 > test-metrics.R: 3 4 > test-metrics.R: 5 > test-metrics.R: 6 > test-metrics.R: 7 > test-metrics.R: 8 > test-metrics.R: 9 10 11 12 > test-metrics.R: Optimizing with step length 0.5218. > test-metrics.R: The log-likelihood improved by 2.9696. > test-metrics.R: Estimating equations are not within tolerance region. > test-metrics.R: Iteration 3 of at most 60: > test-miss.CD.R: Convergence test P-value:2.8e-153 > test-miss.CD.R: 1 > test-miss.CD.R: 2 > test-miss.CD.R: The log-likelihood improved by < 0.0001. > test-miss.CD.R: Iteration 9 of at most 60: > test-miss.CD.R: Convergence test P-value:1e-154 > test-miss.CD.R: 1 > test-miss.CD.R: 2 > test-miss.CD.R: The log-likelihood improved by < 0.0001. > test-metrics.R: 1 2 3 Optimizing with step length 0.8048. > test-miss.CD.R: Iteration 10 of at most 60: > test-metrics.R: The log-likelihood improved by 1.8226. > test-metrics.R: Estimating equations are not within tolerance region. > test-metrics.R: Iteration 4 of at most 60: > test-miss.CD.R: Convergence test P-value:2.5e-153 > test-miss.CD.R: 1 > test-miss.CD.R: 2 > test-miss.CD.R: The log-likelihood improved by < 0.0001. > test-miss.CD.R: Iteration 11 of at most 60: > test-metrics.R: 1 > test-metrics.R: Optimizing with step length 1.0000. > test-metrics.R: The log-likelihood improved by 0.2705. > test-metrics.R: Estimating equations are not within tolerance region. > test-metrics.R: Iteration 5 of at most 60: > test-miss.CD.R: Convergence test P-value:8e-150 > test-miss.CD.R: 1 > test-miss.CD.R: 2 > test-miss.CD.R: The log-likelihood improved by < 0.0001. > test-miss.CD.R: Iteration 12 of at most 60: > test-miss.CD.R: Convergence test P-value:6.7e-158 > test-miss.CD.R: 1 > test-miss.CD.R: 2 > test-metrics.R: 1 > test-metrics.R: Optimizing with step length 1.0000. > test-miss.CD.R: The log-likelihood improved by < 0.0001. > test-miss.CD.R: Iteration 13 of at most 60: > test-metrics.R: The log-likelihood improved by 0.0012. > test-metrics.R: Convergence test p-value: 0.0099. Converged with 99% confidence. > test-metrics.R: Finished MCMLE. > test-metrics.R: This model was fit using MCMC. To examine model diagnostics and check > test-metrics.R: for degeneracy, use the mcmc.diagnostics() function. > test-miss.CD.R: Convergence test P-value:8.6e-156 > test-miss.CD.R: 1 > test-miss.CD.R: 2 > test-miss.CD.R: The log-likelihood improved by < 0.0001. > test-miss.CD.R: Iteration 14 of at most 60: > test-metrics.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-metrics.R: Iteration 1 of at most 60: > test-miss.CD.R: Convergence test P-value:7.9e-162 > test-miss.CD.R: 1 > test-miss.CD.R: 2 > test-miss.CD.R: The log-likelihood improved by < 0.0001. > test-miss.CD.R: Iteration 15 of at most 60: > test-miss.CD.R: Convergence test P-value:1.3e-158 > test-miss.CD.R: 1 > test-miss.CD.R: 2 > test-miss.CD.R: The log-likelihood improved by < 0.0001. > test-miss.CD.R: Iteration 16 of at most 60: > test-miss.CD.R: Convergence test P-value:1.1e-159 > test-miss.CD.R: 1 > test-miss.CD.R: 2 > test-miss.CD.R: The log-likelihood improved by < 0.0001. > test-miss.CD.R: Iteration 17 of at most 60: > test-miss.CD.R: Convergence test P-value:3.3e-159 > test-miss.CD.R: 1 > test-miss.CD.R: 2 > test-miss.CD.R: The log-likelihood improved by < 0.0001. > test-miss.CD.R: Iteration 18 of at most 60: > test-miss.CD.R: Convergence test P-value:6.6e-157 > test-miss.CD.R: 1 > test-miss.CD.R: 2 > test-miss.CD.R: The log-likelihood improved by < 0.0001. > test-miss.CD.R: Iteration 19 of at most 60: > test-metrics.R: 1 > test-metrics.R: 2 3 > test-metrics.R: 4 > test-metrics.R: 5 > test-metrics.R: 6 7 > test-metrics.R: 8 > test-metrics.R: 9 10 > test-metrics.R: 11 > test-metrics.R: 12 > test-metrics.R: 13 > test-metrics.R: Optimizing with step length 0.4397. > test-metrics.R: The log-likelihood improved by 3.0119. > test-metrics.R: Estimating equations are not within tolerance region. > test-metrics.R: Iteration 2 of at most 60: > test-miss.CD.R: Convergence test P-value:2.5e-163 > test-miss.CD.R: 1 > test-miss.CD.R: 2 > test-miss.CD.R: The log-likelihood improved by < 0.0001. > test-miss.CD.R: Iteration 20 of at most 60: > test-miss.CD.R: Convergence test P-value:3.5e-156 > test-miss.CD.R: 1 > test-miss.CD.R: 2 > test-miss.CD.R: The log-likelihood improved by < 0.0001. > test-miss.CD.R: Iteration 21 of at most 60: > test-metrics.R: 1 > test-metrics.R: 2 3 > test-metrics.R: 4 > test-metrics.R: 5 > test-metrics.R: 6 7 > test-metrics.R: 8 > test-metrics.R: 9 10 > test-metrics.R: 11 > test-metrics.R: 12 > test-metrics.R: Optimizing with step length 0.6225. > test-metrics.R: The log-likelihood improved by 3.6934. > test-metrics.R: Estimating equations are not within tolerance region. > test-metrics.R: Iteration 3 of at most 60: > test-miss.CD.R: Convergence test P-value:2.3e-153 > test-miss.CD.R: 1 > test-miss.CD.R: 2 > test-miss.CD.R: The log-likelihood improved by < 0.0001. > test-miss.CD.R: Iteration 22 of at most 60: > test-miss.CD.R: Convergence test P-value:1e-157 > test-miss.CD.R: 1 > test-miss.CD.R: 2 > test-metrics.R: 1 > test-metrics.R: Optimizing with step length 1.0000. > test-metrics.R: The log-likelihood improved by 1.0488. > test-metrics.R: Estimating equations are not within tolerance region. > test-metrics.R: Iteration 4 of at most 60: > test-metrics.R: 1 > test-metrics.R: Optimizing with step length 1.0000. > test-metrics.R: The log-likelihood improved by 0.0821. > test-metrics.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-metrics.R: Finished MCMLE. > test-miss.CD.R: The log-likelihood improved by < 0.0001. > test-miss.CD.R: Iteration 23 of at most 60: > test-metrics.R: This model was fit using MCMC. To examine model diagnostics and check > test-metrics.R: for degeneracy, use the mcmc.diagnostics() function. > test-miss.CD.R: Convergence test P-value:1.4e-160 > test-miss.CD.R: 1 > test-miss.CD.R: 2 > test-miss.CD.R: The log-likelihood improved by < 0.0001. > test-miss.CD.R: Iteration 24 of at most 60: > test-miss.R: n=20, density=0.1, missing=0.05 > test-miss.CD.R: Convergence test P-value:2.1e-158 > test-miss.CD.R: 1 > test-miss.CD.R: 2 > test-miss.CD.R: The log-likelihood improved by < 0.0001. > test-miss.CD.R: Iteration 25 of at most 60: > test-miss.R: Correct estimate = > test-miss.R: -2.118156 with log-likelihood -120.6883 . > test-miss.CD.R: Convergence test P-value:1.3e-150 > test-miss.CD.R: 1 > test-miss.CD.R: 2 > test-miss.CD.R: The log-likelihood improved by < 0.0001. > test-miss.CD.R: Iteration 26 of at most 60: > test-miss.R: Starting maximum pseudolikelihood estimation (MPLE): > test-miss.R: Obtaining the responsible dyads. > test-miss.R: Evaluating the predictor and response matrix. > test-miss.R: Maximizing the pseudolikelihood. > test-miss.R: Finished MPLE. > test-miss.R: Evaluating log-likelihood at the estimate. > test-miss.R: > test-miss.R: MPLE estimate = > test-miss.R: -2.118156 with log-likelihood -120.6883 OK. > test-miss.R: Evaluating network in model. > test-miss.CD.R: Convergence test P-value:4.5e-158 > test-miss.R: Initializing unconstrained Metropolis-Hastings proposal: > test-miss.CD.R: 1 > test-miss.CD.R: 2 > test-miss.R: 'ergm:MH_SPDyad'. > test-miss.R: Initializing constrained Metropolis-Hastings proposal: > test-miss.CD.R: The log-likelihood improved by < 0.0001. > test-miss.CD.R: Iteration 27 of at most 60: > test-miss.R: 'ergm:MH_SPDyad'. > test-miss.R: Initializing model... > test-miss.R: Model initialized. > test-miss.R: Using initial method 'MPLE'. > test-miss.R: Initial parameters provided by caller: > test-miss.R: edges > test-miss.R: -1.118156 > test-miss.R: number of free parameters: 1 > test-miss.R: number of fixed parameters: 0 > test-miss.R: Fitting initial model. > test-miss.R: Imputing 26 dyads is required. > test-miss.R: Imputing 3 edges at random. > test-miss.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-miss.R: Density guard set to 10000 from an initial count of 41 edges. > test-miss.CD.R: Convergence test P-value:2.8e-154 > test-miss.CD.R: 1 > test-miss.R: > test-miss.R: Iteration 1 of at most 60 with parameter: > test-miss.R: edges > test-miss.R: -1.118156 > test-miss.R: Starting unconstrained MCMC... > test-miss.CD.R: 2 > test-miss.CD.R: The log-likelihood improved by < 0.0001. > test-miss.CD.R: Iteration 28 of at most 60: > test-miss.CD.R: Convergence test P-value:8.6e-160 > test-miss.CD.R: 1 > test-miss.CD.R: 2 > test-miss.CD.R: The log-likelihood improved by < 0.0001. > test-miss.CD.R: Iteration 29 of at most 60: > test-miss.R: Back from unconstrained MCMC. > test-miss.R: Starting constrained MCMC... > test-miss.CD.R: Convergence test P-value:2.9e-157 > test-miss.CD.R: 1 > test-miss.CD.R: 2 > test-miss.CD.R: The log-likelihood improved by < 0.0001. > test-miss.CD.R: Iteration 30 of at most 60: > test-miss.R: Back from constrained MCMC. > test-miss.R: New interval = 512. > test-miss.R: New constrained interval = 256. > test-miss.R: Average estimating function values: > test-miss.R: edges > test-miss.R: 49.45267 > test-miss.R: Starting MCMLE Optimization... > test-miss.R: 1 > test-miss.R: 2 > test-miss.R: 3 > test-miss.R: 4 > test-miss.R: 5 > test-miss.R: 6 7 > test-miss.R: 8 > test-miss.R: 9 > test-miss.R: 10 > test-miss.R: 11 12 > test-miss.R: Optimizing with step length 0.4099. > test-miss.R: Using lognormal metric (see control.ergm function). > test-miss.R: Using log-normal approx with missing (no optim) > test-miss.R: The log-likelihood improved by 2.5936. > test-miss.R: Estimating equations are not within tolerance region. > test-miss.R: > test-miss.R: Iteration 2 of at most 60 with parameter: > test-miss.R: edges > test-miss.R: -1.374066 > test-miss.R: Starting unconstrained MCMC... > test-miss.CD.R: Convergence test P-value:1.3e-156 > test-miss.CD.R: 1 > test-miss.CD.R: 2 > test-miss.CD.R: The log-likelihood improved by < 0.0001. > test-miss.CD.R: Iteration 31 of at most 60: > test-miss.CD.R: Convergence test P-value:1.9e-155 > test-miss.CD.R: 1 > test-miss.CD.R: 2 > test-miss.CD.R: The log-likelihood improved by < 0.0001. > test-miss.CD.R: Iteration 32 of at most 60: > test-miss.CD.R: Convergence test P-value:1.5e-156 > test-miss.CD.R: 1 > test-miss.CD.R: 2 > test-miss.R: Back from unconstrained MCMC. > test-miss.R: Starting constrained MCMC... > test-miss.CD.R: The log-likelihood improved by < 0.0001. > test-miss.CD.R: Iteration 33 of at most 60: > test-miss.CD.R: Convergence test P-value:1e-153 > test-miss.CD.R: 1 > test-miss.CD.R: 2 > test-miss.R: Back from constrained MCMC. > test-miss.R: New interval = 256. > test-miss.R: New constrained interval = 128. > test-miss.R: Average estimating function values: > test-miss.R: edges > test-miss.R: 33.36008 > test-miss.R: Starting MCMLE Optimization... > test-miss.R: 1 > test-miss.R: 2 > test-miss.R: 3 > test-miss.R: 4 5 > test-miss.R: 6 > test-miss.R: 7 8 > test-miss.R: 9 > test-miss.R: 10 > test-miss.R: 11 > test-miss.R: 12 > test-miss.R: Optimizing with step length 0.4981. > test-miss.R: Using lognormal metric (see control.ergm function). > test-miss.R: Using log-normal approx with missing (no optim) > test-miss.R: The log-likelihood improved by 2.2702. > test-miss.R: Distance from origin on tolerance region scale: 192.073 (previously 422.077). > test-miss.R: Estimating equations are not within tolerance region. > test-miss.R: > test-miss.R: Iteration 3 of at most 60 with parameter: > test-miss.R: edges > test-miss.R: -1.647302 > test-miss.R: Starting unconstrained MCMC... > test-miss.CD.R: The log-likelihood improved by < 0.0001. > test-miss.CD.R: Iteration 34 of at most 60: > test-miss.CD.R: Convergence test P-value:2.5e-165 > test-miss.CD.R: 1 > test-miss.R: Back from unconstrained MCMC. > test-miss.R: Starting constrained MCMC... > test-miss.CD.R: 2 > test-miss.CD.R: The log-likelihood improved by < 0.0001. > test-miss.CD.R: Iteration 35 of at most 60: > test-miss.CD.R: Convergence test P-value:3.7e-164 > test-miss.CD.R: 1 > test-miss.CD.R: 2 > test-miss.CD.R: The log-likelihood improved by < 0.0001. > test-miss.CD.R: Iteration 36 of at most 60: > test-miss.R: Back from constrained MCMC. > test-miss.R: New interval = 128. > test-miss.R: New constrained interval = 128. > test-miss.R: Average estimating function values: > test-miss.R: edges > test-miss.R: 17.50766 > test-miss.R: Starting MCMLE Optimization... > test-miss.R: 1 > test-miss.R: 2 > test-miss.R: 3 > test-miss.R: 4 5 > test-miss.R: Optimizing with step length 0.7736. > test-miss.R: Using lognormal metric (see control.ergm function). > test-miss.R: Using log-normal approx with missing (no optim) > test-miss.R: The log-likelihood improved by 2.0776. > test-miss.R: Distance from origin on tolerance region scale: 71.38353 (previously 259.1767). > test-miss.R: Estimating equations are not within tolerance region. > test-miss.R: > test-miss.R: Iteration 4 of at most 60 with parameter: > test-miss.R: edges > test-miss.R: -1.95412 > test-miss.R: Starting unconstrained MCMC... > test-miss.CD.R: Convergence test P-value:3.5e-165 > test-miss.CD.R: 1 2 > test-miss.R: Back from unconstrained MCMC. > test-miss.R: Starting constrained MCMC... > test-miss.CD.R: The log-likelihood improved by < 0.0001. > test-miss.CD.R: Iteration 37 of at most 60: > test-miss.R: Back from constrained MCMC. > test-miss.R: New interval = 64. > test-miss.R: New constrained interval = 64. > test-miss.R: Average estimating function values: > test-miss.R: edges > test-miss.R: 5.621399 > test-miss.R: Starting MCMLE Optimization... > test-miss.R: 1 > test-miss.R: Optimizing with step length 1.0000. > test-miss.R: Using lognormal metric (see control.ergm function). > test-miss.R: Using log-normal approx with missing (no optim) > test-miss.R: The log-likelihood improved by 0.3258. > test-miss.R: Distance from origin on tolerance region scale: 6.488425 (previously 62.9371). > test-miss.R: Estimating equations are not within tolerance region. > test-miss.R: > test-miss.R: Iteration 5 of at most 60 with parameter: > test-miss.R: edges > test-miss.R: -2.070021 > test-miss.R: Starting unconstrained MCMC... > test-miss.CD.R: Convergence test P-value:3.8e-167 > test-miss.CD.R: 1 > test-miss.CD.R: 2 > test-miss.R: Back from unconstrained MCMC. > test-miss.R: Starting constrained MCMC... > test-miss.R: Back from constrained MCMC. > test-miss.R: New interval = 32. > test-miss.R: New constrained interval = 32. > test-miss.R: Average estimating function values: > test-miss.R: edges > test-miss.R: 1.853909 > test-miss.R: Starting MCMLE Optimization... > test-miss.R: 1 > test-miss.R: Optimizing with step length 1.0000. > test-miss.R: Using lognormal metric (see control.ergm function). > test-miss.R: Using log-normal approx with missing (no optim) > test-miss.R: Starting MCMC s.e. computation. > test-miss.R: The log-likelihood improved by 0.0589. > test-miss.R: Distance from origin on tolerance region scale: 1.17581 (previously 10.81058). > test-miss.R: Test statistic: T^2 = 11.54078, with 1 free parameter(s) and 179.1884 degrees of freedom. > test-miss.R: Convergence test p-value: 0.0008. Converged with 99% confidence. > test-miss.R: Finished MCMLE. > test-miss.R: Evaluating log-likelihood at the estimate. > test-miss.R: Initializing model to obtain the list of dyad-independent terms... > test-miss.R: Fitting the dyad-independent submodel... > test-miss.CD.R: The log-likelihood improved by < 0.0001. > test-miss.CD.R: Iteration 38 of at most 60: > test-miss.CD.R: Convergence test P-value:4.3e-166 > test-miss.CD.R: 1 > test-miss.CD.R: 2 > test-miss.R: Dyad-independent submodel MLE has likelihood -120.6883 at: > test-miss.R: [1] -2.118156 0.000000 > test-miss.R: Bridging between the dyad-independent submodel and the full model... > test-miss.R: Setting up bridge sampling... > test-miss.R: Initializing model and proposals... > test-miss.CD.R: The log-likelihood improved by < 0.0001. > test-miss.CD.R: Iteration 39 of at most 60: > test-miss.R: Model and proposals initialized. > test-miss.R: Initializing constrained model and proposals... > test-miss.R: Constrained model and proposals initialized. > test-miss.R: Using 16 bridges: Running theta=[-2.133081, 0.000000]. > test-miss.CD.R: Convergence test P-value:1.8e-166 > test-miss.CD.R: 1 > test-miss.CD.R: 2 > test-miss.R: Running theta=[-2.132118, 0.000000]. > test-miss.R: Running theta=[-2.131155, 0.000000]. > test-miss.R: Running theta=[-2.130192, 0.000000]. > test-miss.R: Running theta=[-2.129229, 0.000000]. > test-miss.CD.R: The log-likelihood improved by < 0.0001. > test-miss.CD.R: Iteration 40 of at most 60: > test-miss.R: Running theta=[-2.128267, 0.000000]. > test-miss.R: Running theta=[-2.127304, 0.000000]. > test-miss.R: Running theta=[-2.126341, 0.000000]. > test-miss.R: Running theta=[-2.125378, 0.000000]. > test-miss.R: Running theta=[-2.124415, 0.000000]. > test-miss.R: Running theta=[-2.123452, 0.000000]. > test-miss.R: Running theta=[-2.122489, 0.000000]. > test-miss.CD.R: Convergence test P-value:1.4e-154 > test-miss.R: Running theta=[-2.121526, 0.000000]. > test-miss.CD.R: 1 > test-miss.CD.R: 2 > test-miss.R: Running theta=[-2.120563, 0.000000]. > test-miss.R: Running theta=[-2.1196, 0.0000]. > test-miss.CD.R: The log-likelihood improved by < 0.0001. > test-miss.CD.R: Iteration 41 of at most 60: > test-miss.R: Running theta=[-2.118637, 0.000000]. > test-miss.R: . > test-miss.R: Bridge sampling finished. Collating... > test-miss.R: Estimated standard error (0.009575496) above target (0.005). Drawing additional samples. > test-miss.R: Running theta=[-2.119005, 0.000000]. > test-miss.R: Running theta=[-2.119968, 0.000000]. > test-miss.R: Running theta=[-2.120931, 0.000000]. > test-miss.R: Running theta=[-2.121894, 0.000000]. > test-miss.R: Running theta=[-2.122857, 0.000000]. > test-miss.R: Running theta=[-2.12382, 0.00000]. > test-miss.R: Running theta=[-2.124783, 0.000000]. > test-miss.CD.R: Convergence test P-value:1.3e-139 > test-miss.R: Running theta=[-2.125746, 0.000000]. > test-miss.R: Running theta=[-2.126709, 0.000000]. > test-miss.CD.R: 1 > test-miss.CD.R: 2 > test-miss.R: Running theta=[-2.127671, 0.000000]. > test-miss.R: Running theta=[-2.128634, 0.000000]. > test-miss.R: Running theta=[-2.129597, 0.000000]. > test-miss.CD.R: The log-likelihood improved by < 0.0001. > test-miss.CD.R: Iteration 42 of at most 60: > test-miss.R: Running theta=[-2.13056, 0.00000]. > test-miss.R: Running theta=[-2.131523, 0.000000]. > test-miss.R: Running theta=[-2.132486, 0.000000]. > test-miss.R: Running theta=[-2.133449, 0.000000]. > test-miss.R: . > test-miss.R: Bridge sampling finished. Collating... > test-miss.R: Estimated standard error (0.007542247) above target (0.005). Drawing additional samples. > test-miss.R: Running theta=[-2.132854, 0.000000]. > test-miss.R: Running theta=[-2.131891, 0.000000]. > test-miss.R: Running theta=[-2.130928, 0.000000]. > test-miss.R: Running theta=[-2.129965, 0.000000]. > test-miss.CD.R: Convergence test P-value:7.6e-160 > test-miss.CD.R: 1 > test-miss.R: Running theta=[-2.129002, 0.000000]. > test-miss.CD.R: 2 > test-miss.R: Running theta=[-2.128039, 0.000000]. > test-miss.R: Running theta=[-2.127076, 0.000000]. > test-miss.CD.R: The log-likelihood improved by < 0.0001. > test-miss.CD.R: Iteration 43 of at most 60: > test-miss.R: Running theta=[-2.126113, 0.000000]. > test-miss.R: Running theta=[-2.125151, 0.000000]. > test-miss.R: Running theta=[-2.124188, 0.000000]. > test-miss.R: Running theta=[-2.123225, 0.000000]. > test-miss.R: Running theta=[-2.122262, 0.000000]. > test-miss.CD.R: Convergence test P-value:3e-152 > test-miss.CD.R: 1 2 > test-miss.R: Running theta=[-2.121299, 0.000000]. > test-miss.CD.R: The log-likelihood improved by < 0.0001. > test-miss.R: Running theta=[-2.120336, 0.000000]. > test-miss.CD.R: Iteration 44 of at most 60: > test-miss.R: Running theta=[-2.119373, 0.000000]. > test-miss.R: Running theta=[-2.11841, 0.00000]. > test-miss.R: . > test-miss.R: Bridge sampling finished. Collating... > test-miss.R: Estimated standard error (0.006188692) above target (0.005). Drawing additional samples. > test-miss.R: Running theta=[-2.118778, 0.000000]. > test-miss.R: Running theta=[-2.119741, 0.000000]. > test-miss.R: Running theta=[-2.120704, 0.000000]. > test-miss.R: Running theta=[-2.121667, 0.000000]. > test-miss.R: Running theta=[-2.12263, 0.00000]. > test-miss.CD.R: Convergence test P-value:3.1e-154 > test-miss.CD.R: 1 2 > test-miss.R: Running theta=[-2.123593, 0.000000]. > test-miss.R: Running theta=[-2.124555, 0.000000]. > test-miss.CD.R: The log-likelihood improved by < 0.0001. > test-miss.R: Running theta=[-2.125518, 0.000000]. > test-miss.CD.R: Iteration 45 of at most 60: > test-miss.R: Running theta=[-2.126481, 0.000000]. > test-miss.R: Running theta=[-2.127444, 0.000000]. > test-miss.R: Running theta=[-2.128407, 0.000000]. > test-miss.R: Running theta=[-2.12937, 0.00000]. > test-miss.R: Running theta=[-2.130333, 0.000000]. > test-miss.CD.R: Convergence test P-value:1.9e-150 > test-miss.CD.R: 1 > test-miss.CD.R: 2 > test-miss.R: Running theta=[-2.131296, 0.000000]. > test-miss.R: Running theta=[-2.132259, 0.000000]. > test-miss.CD.R: The log-likelihood improved by < 0.0001. > test-miss.CD.R: Iteration 46 of at most 60: > test-miss.R: Running theta=[-2.133222, 0.000000]. > test-miss.R: . > test-miss.R: Bridge sampling finished. Collating... > test-miss.R: Estimated standard error (0.005396227) above target (0.005). Drawing additional samples. > test-miss.R: Running theta=[-2.132626, 0.000000]. > test-miss.R: Running theta=[-2.131664, 0.000000]. > test-miss.R: Running theta=[-2.130701, 0.000000]. > test-miss.R: Running theta=[-2.129738, 0.000000]. > test-miss.CD.R: Convergence test P-value:8.8e-148 > test-miss.CD.R: 1 > test-miss.CD.R: 2 > test-miss.R: Running theta=[-2.128775, 0.000000]. > test-miss.CD.R: The log-likelihood improved by < 0.0001. > test-miss.CD.R: Iteration 47 of at most 60: > test-miss.R: Running theta=[-2.127812, 0.000000]. > test-miss.R: Running theta=[-2.126849, 0.000000]. > test-miss.R: Running theta=[-2.125886, 0.000000]. > test-miss.R: Running theta=[-2.124923, 0.000000]. > test-miss.R: Running theta=[-2.12396, 0.00000]. > test-miss.CD.R: Convergence test P-value:1.8e-153 > test-miss.CD.R: 1 > test-miss.CD.R: 2 > test-miss.R: Running theta=[-2.122997, 0.000000]. > test-miss.CD.R: The log-likelihood improved by < 0.0001. > test-miss.CD.R: Iteration 48 of at most 60: > test-miss.R: Running theta=[-2.122035, 0.000000]. > test-miss.R: Running theta=[-2.121072, 0.000000]. > test-miss.R: Running theta=[-2.120109, 0.000000]. > test-miss.R: Running theta=[-2.119146, 0.000000]. > test-miss.R: Running theta=[-2.118183, 0.000000]. > test-miss.R: . > test-miss.R: Bridge sampling finished. Collating... > test-miss.R: Bridging finished. > test-miss.R: > test-miss.R: This model was fit using MCMC. To examine model diagnostics and check > test-miss.R: for degeneracy, use the mcmc.diagnostics() function. > test-miss.CD.R: Convergence test P-value:4.8e-158 > test-miss.CD.R: 1 > test-miss.CD.R: 2 > test-miss.CD.R: The log-likelihood improved by < 0.0001. > test-miss.CD.R: Iteration 49 of at most 60: > test-miss.CD.R: Convergence test P-value:6e-154 > test-miss.CD.R: 1 > test-miss.CD.R: 2 > test-miss.CD.R: The log-likelihood improved by < 0.0001. > test-miss.CD.R: Iteration 50 of at most 60: > test-miss.CD.R: Convergence test P-value:1.4e-158 > test-miss.CD.R: 1 > test-miss.CD.R: 2 > test-miss.CD.R: The log-likelihood improved by < 0.0001. > test-miss.CD.R: Iteration 51 of at most 60: > test-miss.CD.R: Convergence test P-value:1.2e-150 > test-miss.CD.R: 1 > test-miss.CD.R: 2 > test-miss.R: Sample statistics summary: > test-miss.R: > test-miss.R: Iterations = 1728:32768 > test-miss.R: Thinning interval = 64 > test-miss.R: Number of chains = 1 > test-miss.R: Sample size per chain = 486 > test-miss.R: > test-miss.R: 1. Empirical mean and standard deviation for each variable, > test-miss.R: plus standard error of the mean: > test-miss.R: > test-miss.R: Mean SD Naive SE Time-series SE > test-miss.R: 1.8539 5.6566 0.2566 0.4463 > test-miss.R: > test-miss.R: 2. Quantiles for each variable: > test-miss.R: > test-miss.R: 2.5% 25% 50% 75% 97.5% > test-miss.R: -8.881 -1.881 2.119 5.119 14.119 > test-miss.R: > test-miss.R: Constrained sample statistics summary: > test-miss.R: > test-miss.CD.R: The log-likelihood improved by < 0.0001. > test-miss.R: Iterations = 896:16384 > test-miss.R: Thinning interval = 64 > test-miss.R: Number of chains = 1 > test-miss.R: Sample size per chain = 243 > test-miss.R: > test-miss.R: 1. Empirical mean and standard deviation for each variable, > test-miss.R: plus standard error of the mean: > test-miss.R: > test-miss.R: Mean SD Naive SE Time-series SE > test-miss.R: -2.129e-17 1.663e+00 1.067e-01 1.067e-01 > test-miss.R: > test-miss.R: 2. Quantiles for each variable: > test-miss.R: > test-miss.R: 2.5% 25% 50% 75% 97.5% > test-miss.R: -2.8807 -1.3807 0.1193 1.1193 4.0693 > test-miss.R: > test-miss.R: > test-miss.R: Are unconstrained sample statistics significantly different from constrained? > test-miss.CD.R: Iteration 52 of at most 60: > test-miss.R: edges (Omni) > test-miss.R: diff. 1.853909e+00 NA > test-miss.R: test stat. 4.040502e+00 1.632566e+01 > test-miss.R: P-val. 5.333689e-05 7.904382e-05 > test-miss.R: > test-miss.R: Sample statistics cross-correlations: > test-miss.R: edges > test-miss.R: edges 1 > test-miss.R: Constrained sample statistics cross-correlations: > test-miss.R: edges > test-miss.R: edges 1 > test-miss.R: > test-miss.R: Sample statistics auto-correlation: > test-miss.R: Chain 1 > test-miss.R: edges > test-miss.R: Lag 0 1.00000000 > test-miss.R: Lag 64 0.50229634 > test-miss.R: Lag 128 0.27968825 > test-miss.R: Lag 192 0.14989734 > test-miss.R: Lag 256 0.08553620 > test-miss.R: Lag 320 0.01488518 > test-miss.R: Constrained sample statistics auto-correlation: > test-miss.R: Chain 1 > test-miss.R: edges > test-miss.R: Lag 0 1.00000000 > test-miss.R: Lag 64 -0.04999730 > test-miss.R: Lag 128 0.05488745 > test-miss.R: Lag 192 -0.03080175 > test-miss.R: Lag 256 0.03734623 > test-miss.R: Lag 320 0.01289319 > test-miss.R: > test-miss.R: Sample statistics burn-in diagnostic (Geweke): > test-miss.R: Chain 1 > test-miss.R: > test-miss.R: Fraction in 1st window = 0.1 > test-miss.R: Fraction in 2nd window = 0.5 > test-miss.R: > test-miss.R: edges > test-miss.R: -0.4658741 > test-miss.R: > test-miss.R: Individual P-values (lower = worse): > test-miss.R: edges > test-miss.R: 0.6413056 > test-miss.R: Joint P-value (lower = worse): 0.5503774 > test-miss.R: Sample statistics burn-in diagnostic (Geweke): > test-miss.R: Chain 1 > test-miss.R: > test-miss.R: Fraction in 1st window = 0.1 > test-miss.R: Fraction in 2nd window = 0.5 > test-miss.R: > test-miss.R: edges > test-miss.R: -0.2553269 > test-miss.R: > test-miss.R: P-values (lower = worse): > test-miss.R: edges > test-miss.R: 0.7984706 > test-miss.R: Joint P-value (lower = worse): 0.5503774 . > test-miss.R: > test-miss.R: Note: MCMC diagnostics shown here are from the last round of > test-miss.R: simulation, prior to computation of final parameter estimates. > test-miss.R: Because the final estimates are refinements of those used for this > test-miss.R: simulation run, these diagnostics may understate model performance. > test-miss.R: To directly assess the performance of the final model on in-model > test-miss.R: statistics, please use the GOF command: gof(ergmFitObject, > test-miss.R: GOF=~model). > test-miss.R: > test-miss.R: MCMCMLE estimate = -2.133563 with log-likelihood -120.7039 OK. > test-miss.CD.R: Convergence test P-value:2.2e-161 > test-miss.CD.R: 1 2 > test-miss.CD.R: The log-likelihood improved by < 0.0001. > test-miss.CD.R: Iteration 53 of at most 60: > test-miss.CD.R: Convergence test P-value:5.8e-145 > test-miss.CD.R: 1 > test-miss.CD.R: 2 > test-miss.R: Correct estimate = -1.663142 with log-likelihood -79.82064 . > test-miss.CD.R: The log-likelihood improved by < 0.0001. > test-miss.CD.R: Iteration 54 of at most 60: > test-miss.CD.R: Convergence test P-value:5.8e-163 > test-miss.CD.R: 1 > test-miss.CD.R: 2 > test-miss.R: Starting maximum pseudolikelihood estimation (MPLE): > test-miss.R: Obtaining the responsible dyads. > test-miss.R: Evaluating the predictor and response matrix. > test-miss.R: Maximizing the pseudolikelihood. > test-miss.R: Finished MPLE. > test-miss.R: Evaluating log-likelihood at the estimate. > test-miss.R: > test-miss.R: MPLE estimate = -1.663142 with log-likelihood -79.82064 OK. > test-miss.CD.R: The log-likelihood improved by < 0.0001. > test-miss.CD.R: Iteration 55 of at most 60: > test-miss.R: Evaluating network in model. > test-miss.R: Initializing unconstrained Metropolis-Hastings proposal: > test-miss.R: 'ergm:MH_SPDyad'. > test-miss.R: Initializing constrained Metropolis-Hastings proposal: > test-miss.CD.R: Convergence test P-value:1.3e-156 > test-miss.CD.R: 1 > test-miss.R: 'ergm:MH_SPDyad'. > test-miss.R: Initializing model... > test-miss.CD.R: 2 > test-miss.R: Model initialized. > test-miss.R: Using initial method 'MPLE'. > test-miss.R: Initial parameters provided by caller: > test-miss.R: > test-miss.R: edges > test-miss.R: -0.6631421 > test-miss.R: number of free parameters: 1 > test-miss.R: number of fixed parameters: 0 > test-miss.R: Fitting initial model. > test-miss.R: Imputing 8 dyads is required. > test-miss.R: Imputing 1 edges at random. > test-miss.CD.R: The log-likelihood improved by < 0.0001. > test-miss.CD.R: Iteration 56 of at most 60: > test-miss.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-miss.R: Density guard set to 10000 from an initial count of 30 edges. > test-miss.R: > test-miss.R: Iteration 1 of at most 60 with parameter: > test-miss.R: edges > test-miss.R: -0.6631421 > test-miss.R: Starting unconstrained MCMC... > test-miss.CD.R: Convergence test P-value:2e-150 > test-miss.CD.R: 1 > test-miss.CD.R: 2 > test-miss.CD.R: The log-likelihood improved by < 0.0001. > test-miss.CD.R: Iteration 57 of at most 60: > test-miss.R: Back from unconstrained MCMC. > test-miss.R: Starting constrained MCMC... > test-miss.CD.R: Convergence test P-value:2.4e-151 > test-miss.CD.R: 1 > test-miss.CD.R: 2 > test-miss.CD.R: The log-likelihood improved by < 0.0001. > test-miss.CD.R: Iteration 58 of at most 60: > test-miss.R: Back from constrained MCMC. > test-miss.R: New interval = 512. > test-miss.R: New constrained interval = 256. > test-miss.R: Average estimating function values: > test-miss.R: edges > test-miss.R: 33.15638 > test-miss.R: Starting MCMLE Optimization... > test-miss.R: 1 > test-miss.R: 2 > test-miss.R: 3 > test-miss.R: 4 > test-miss.R: 5 6 > test-miss.R: 7 > test-miss.R: 8 > test-miss.R: Optimizing with step length 0.5368. > test-miss.R: Using lognormal metric (see control.ergm function). > test-miss.R: Using log-normal approx with missing (no optim) > test-miss.R: The log-likelihood improved by 4.3000. > test-miss.R: Estimating equations are not within tolerance region. > test-miss.R: > test-miss.R: Iteration 2 of at most 60 with parameter: > test-miss.R: edges > test-miss.R: -1.146333 > test-miss.R: Starting unconstrained MCMC... > test-miss.CD.R: Convergence test P-value:1.2e-168 > test-miss.CD.R: 1 > test-miss.CD.R: 2 > test-miss.CD.R: The log-likelihood improved by < 0.0001. > test-miss.CD.R: Iteration 59 of at most 60: > test-miss.R: Back from unconstrained MCMC. > test-miss.R: Starting constrained MCMC... > test-miss.CD.R: Convergence test P-value:3e-159 > test-miss.CD.R: 1 > test-miss.CD.R: 2 > test-miss.R: Back from constrained MCMC. > test-miss.R: New interval = 256. > test-miss.R: New constrained interval = 128. > test-miss.R: Average estimating function values: > test-miss.R: edges > test-miss.R: 14.85185 > test-miss.R: Starting MCMLE Optimization... > test-miss.R: 1 > test-miss.CD.R: The log-likelihood improved by < 0.0001. > test-miss.CD.R: Iteration 60 of at most 60: > test-miss.R: 2 3 > test-miss.R: Optimizing with step length 1.0000. > test-miss.R: Using lognormal metric (see control.ergm function). > test-miss.R: Using log-normal approx with missing (no optim) > test-miss.R: The log-likelihood improved by 3.2552. > test-miss.R: Distance from origin on tolerance region scale: 64.83604 (previously 323.1391). > test-miss.R: Estimating equations are not within tolerance region. > test-miss.R: > test-miss.R: Iteration 3 of at most 60 with parameter: > test-miss.R: edges > test-miss.R: -1.584689 > test-miss.R: Starting unconstrained MCMC... > test-miss.CD.R: Convergence test P-value:6.5e-157 > test-miss.CD.R: 1 > test-miss.CD.R: 2 > test-miss.R: Back from unconstrained MCMC. > test-miss.R: Starting constrained MCMC... > test-miss.CD.R: The log-likelihood improved by < 0.0001. > test-miss.CD.R: Finished CD. > test-miss.CD.R: This model was fit using MCMC. To examine model diagnostics and check > test-miss.CD.R: for degeneracy, use the mcmc.diagnostics() function. > test-miss.R: Back from constrained MCMC. > test-miss.R: New interval = 128. > test-miss.R: New constrained interval = 64. > test-miss.R: Average estimating function values: > test-miss.R: edges > test-miss.R: 2.600823 > test-miss.R: Starting MCMLE Optimization... > test-miss.R: 1 > test-miss.R: Optimizing with step length 1.0000. > test-miss.R: Using lognormal metric (see control.ergm function). > test-miss.R: Using log-normal approx with missing (no optim) > test-miss.R: The log-likelihood improved by 0.1297. > test-miss.R: Distance from origin on tolerance region scale: 2.582218 (previously 84.20395). > test-miss.R: Estimating equations are not within tolerance region. > test-miss.R: > test-miss.R: Iteration 4 of at most 60 with parameter: > test-miss.R: edges > test-miss.R: -1.684393 > test-miss.R: Starting unconstrained MCMC... > test-miss.R: Back from unconstrained MCMC. > test-miss.R: Starting constrained MCMC... > test-mple-cov.R: Starting maximum pseudolikelihood estimation (MPLE): > test-mple-cov.R: Obtaining the responsible dyads. > test-mple-cov.R: Evaluating the predictor and response matrix. > test-mple-cov.R: Maximizing the pseudolikelihood. > test-miss.R: Back from constrained MCMC. > test-miss.R: New interval = 64. > test-miss.R: New constrained interval = 32. > test-miss.R: Average estimating function values: > test-miss.R: edges > test-miss.R: -0.2386831 > test-miss.R: Starting MCMLE Optimization... > test-miss.R: 1 Optimizing with step length 1.0000. > test-miss.R: Using lognormal metric (see control.ergm function). > test-miss.R: Using log-normal approx with missing (no optim) > test-miss.R: Starting MCMC s.e. computation. > test-miss.R: The log-likelihood improved by 0.0011. > test-miss.R: Distance from origin on tolerance region scale: 0.02175454 (previously 2.583022). > test-miss.R: Test statistic: T^2 = 16.95471, with 1 free parameter(s) and 192.1073 degrees of freedom. > test-miss.R: Convergence test p-value: 0.0001. Converged with 99% confidence. > test-miss.R: Finished MCMLE. > test-miss.R: Evaluating log-likelihood at the estimate. Initializing model to obtain the list of dyad-independent terms... > test-miss.R: Fitting the dyad-independent submodel... > test-miss.R: Dyad-independent submodel MLE has likelihood -79.82064 at: > test-miss.R: [1] -1.663142 0.000000 > test-miss.R: Bridging between the dyad-independent submodel and the full model... > test-miss.R: Setting up bridge sampling... > test-miss.R: Initializing model and proposals... > test-miss.R: Model and proposals initialized. > test-miss.R: Initializing constrained model and proposals... > test-miss.R: Constrained model and proposals initialized. > test-miss.R: Using 16 bridges: Running theta=[-1.674863, 0.000000]. > test-miss.R: Running theta=[-1.674107, 0.000000]. > test-miss.R: Running theta=[-1.673351, 0.000000]. > test-miss.R: Running theta=[-1.672594, 0.000000]. > test-miss.R: Running theta=[-1.671838, 0.000000]. > test-miss.R: Running theta=[-1.671082, 0.000000]. > test-miss.R: Running theta=[-1.670326, 0.000000]. > test-miss.R: Running theta=[-1.66957, 0.00000]. > test-miss.R: Running theta=[-1.668813, 0.000000]. > test-miss.R: Running theta=[-1.668057, 0.000000]. > test-miss.R: Running theta=[-1.667301, 0.000000]. > test-miss.R: Running theta=[-1.666545, 0.000000]. > test-miss.R: Running theta=[-1.665789, 0.000000]. > test-miss.R: Running theta=[-1.665033, 0.000000]. > test-miss.R: Running theta=[-1.664276, 0.000000]. > test-miss.R: Running theta=[-1.66352, 0.00000]. > test-miss.R: . > test-miss.R: Bridge sampling finished. Collating... > test-miss.R: Estimated standard error (0.005483681) above target (0.005). Drawing additional samples. > test-miss.R: Running theta=[-1.663809, 0.000000]. > test-miss.R: Running theta=[-1.664565, 0.000000]. > test-miss.R: Running theta=[-1.665321, 0.000000]. > test-miss.R: Running theta=[-1.666078, 0.000000]. > test-miss.R: Running theta=[-1.666834, 0.000000]. > test-miss.R: Running theta=[-1.66759, 0.00000]. > test-miss.R: Running theta=[-1.668346, 0.000000]. > test-miss.R: Running theta=[-1.669102, 0.000000]. > test-miss.R: Running theta=[-1.669858, 0.000000]. > test-miss.R: Running theta=[-1.670615, 0.000000]. > test-miss.R: Running theta=[-1.671371, 0.000000]. > test-miss.R: Running theta=[-1.672127, 0.000000]. > test-miss.R: Running theta=[-1.672883, 0.000000]. > test-miss.R: Running theta=[-1.673639, 0.000000]. > test-miss.R: Running theta=[-1.674396, 0.000000]. > test-miss.R: Running theta=[-1.675152, 0.000000]. > test-miss.R: . > test-miss.R: Bridge sampling finished. Collating... > test-miss.R: Bridging finished. > test-miss.R: > test-miss.R: This model was fit using MCMC. To examine model diagnostics and check > test-miss.R: for degeneracy, use the mcmc.diagnostics() function. > test-miss.R: Sample statistics summary: > test-miss.R: > test-miss.R: Iterations = 1792:32768 > test-miss.R: Thinning interval = 128 > test-miss.R: Number of chains = 1 > test-miss.R: Sample size per chain = 243 > test-miss.R: > test-miss.R: 1. Empirical mean and standard deviation for each variable, > test-miss.R: plus standard error of the mean: > test-miss.R: > test-miss.R: Mean SD Naive SE Time-series SE > test-miss.R: -0.2387 5.2156 0.3346 0.3867 > test-miss.R: > test-miss.R: 2. Quantiles for each variable: > test-miss.R: > test-miss.R: 2.5% 25% 50% 75% 97.5% > test-miss.R: -9.2346 -4.2346 -0.2346 2.7654 11.7154 > test-miss.R: > test-miss.R: Constrained sample statistics summary: > test-miss.R: > test-miss.R: Iterations = 896:16384 > test-miss.R: Thinning interval = 64 > test-miss.R: Number of chains = 1 > test-miss.R: Sample size per chain = 243 > test-miss.R: > test-miss.R: 1. Empirical mean and standard deviation for each variable, > test-miss.R: plus standard error of the mean: > test-miss.R: > test-miss.R: Mean SD Naive SE Time-series SE > test-miss.R: 4.040e-17 1.007e+00 6.463e-02 6.463e-02 > test-miss.R: > test-miss.R: 2. Quantiles for each variable: > test-miss.R: > test-miss.R: 2.5% 25% 50% 75% 97.5% > test-miss.R: -1.2346 -0.7346 -0.2346 0.7654 2.7154 > test-miss.R: > test-miss.R: > test-miss.R: Are unconstrained sample statistics significantly different from constrained? > test-miss.R: edges (Omni) > test-miss.R: diff. -0.2386831 NA > test-miss.R: test stat. -0.6087923 0.3706280 > test-miss.R: P-val. 0.5426621 0.5433813 > test-miss.R: > test-miss.R: Sample statistics cross-correlations: > test-miss.R: edges > test-miss.R: edges 1 > test-miss.R: Constrained sample statistics cross-correlations: > test-miss.R: edges > test-miss.R: edges 1 > test-miss.R: > test-miss.R: Sample statistics auto-correlation: > test-miss.R: Chain 1 > test-miss.R: edges > test-miss.R: Lag 0 1.000000000 > test-miss.R: Lag 128 0.141730657 > test-miss.R: Lag 256 -0.008044799 > test-miss.R: Lag 384 0.039503814 > test-miss.R: Lag 512 0.016265240 > test-miss.R: Lag 640 0.025525909 > test-miss.R: Constrained sample statistics auto-correlation: > test-miss.R: Chain 1 > test-miss.R: edges > test-miss.R: Lag 0 1.00000000 > test-miss.R: Lag 64 -0.03837238 > test-miss.R: Lag 128 0.06102163 > test-miss.R: Lag 192 0.01817600 > test-miss.R: Lag 256 -0.07663989 > test-miss.R: Lag 320 -0.02107378 > test-miss.R: > test-miss.R: Sample statistics burn-in diagnostic (Geweke): > test-miss.R: Chain 1 > test-miss.R: > test-miss.R: Fraction in 1st window = 0.1 > test-miss.R: Fraction in 2nd window = 0.5 > test-miss.R: > test-miss.R: edges > test-miss.R: -1.387683 > test-miss.R: > test-miss.R: Individual P-values (lower = worse): > test-miss.R: edges > test-miss.R: 0.1652336 > test-miss.R: Joint P-value (lower = worse): 0.2706448 > test-miss.R: Sample statistics burn-in diagnostic (Geweke): > test-miss.R: Chain 1 > test-miss.R: > test-miss.R: Fraction in 1st window = 0.1 > test-miss.R: Fraction in 2nd window = 0.5 > test-miss.R: > test-miss.R: edges > test-miss.R: -0.5702328 > test-miss.R: > test-miss.R: P-values (lower = worse): > test-miss.R: edges > test-miss.R: 0.5685198 > test-miss.R: Joint P-value (lower = worse): 0.2706448 . > test-miss.R: > test-miss.R: Note: MCMC diagnostics shown here are from the last round of > test-miss.R: simulation, prior to computation of final parameter estimates. > test-miss.R: Because the final estimates are refinements of those used for this > test-miss.R: simulation run, these diagnostics may understate model performance. > test-miss.R: To directly assess the performance of the final model on in-model > test-miss.R: statistics, please use the GOF command: gof(ergmFitObject, > test-miss.R: GOF=~model). > test-miss.R: > test-miss.R: MCMCMLE estimate = -1.675241 with log-likelihood -79.81794 OK. > test-mple-cov.R: Estimating Godambe Matrix using 500 simulated networks. > test-miss.R: Correct estimate = -3.157 with log-likelihood -8.355963 . > test-miss.R: Starting maximum pseudolikelihood estimation (MPLE): > test-miss.R: Obtaining the responsible dyads. > test-miss.R: Evaluating the predictor and response matrix. > test-miss.R: Maximizing the pseudolikelihood. > test-miss.R: Finished MPLE. > test-miss.R: Evaluating log-likelihood at the estimate. > test-miss.R: > test-miss.R: MPLE estimate = -3.157 with log-likelihood -8.355963 OK. > test-miss.R: Evaluating network in model. > test-miss.R: Initializing unconstrained Metropolis-Hastings proposal: > test-miss.R: 'ergm:MH_TNT'. > test-miss.R: Initializing constrained Metropolis-Hastings proposal: > test-miss.R: 'ergm:MH_TNT'. > test-miss.R: Initializing model... > test-miss.R: Model initialized. > test-miss.R: Using initial method 'MPLE'. > test-miss.R: Initial parameters provided by caller: > test-miss.R: > test-miss.R: edges > test-miss.R: -2.157 > test-miss.R: number of free parameters: 1 > test-miss.R: number of fixed parameters: 0 > test-miss.R: Fitting initial model. > test-miss.R: Imputing 2 dyads is required. > test-miss.R: Imputing 0 edges at random. > test-miss.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-miss.R: Density guard set to 10000 from an initial count of 2 edges. > test-miss.R: > test-miss.R: Iteration 1 of at most 60 with parameter: > test-miss.R: edges > test-miss.R: -2.157 > test-miss.R: Starting unconstrained MCMC... > test-miss.R: Back from unconstrained MCMC. > test-miss.R: Starting constrained MCMC... > test-miss.R: Back from constrained MCMC. > test-miss.R: New interval = 512. > test-miss.R: New constrained interval = 256. > test-miss.R: Average estimating function values: > test-miss.R: edges > test-miss.R: 2.942387 > test-miss.R: Starting MCMLE Optimization... > test-miss.R: 1 > test-miss.R: Optimizing with step length 1.0000. > test-miss.R: Using lognormal metric (see control.ergm function). > test-miss.R: Using log-normal approx with missing (no optim) > test-miss.R: The log-likelihood improved by 0.8416. > test-miss.R: Estimating equations are not within tolerance region. > test-miss.R: > test-miss.R: Iteration 2 of at most 60 with parameter: > test-miss.R: edges > test-miss.R: -2.729057 > test-miss.R: Starting unconstrained MCMC... > test-miss.R: Back from unconstrained MCMC. > test-miss.R: Starting constrained MCMC... > test-miss.R: Back from constrained MCMC. > test-miss.R: New interval = 256. > test-miss.R: New constrained interval = 128. > test-miss.R: Average estimating function values: > test-miss.R: edges > test-miss.R: 0.9012346 > test-miss.R: Starting MCMLE Optimization... > test-miss.R: 1 > test-miss.R: Optimizing with step length 1.0000. > test-miss.R: Using lognormal metric (see control.ergm function). > test-miss.R: Using log-normal approx with missing (no optim) > test-miss.R: The log-likelihood improved by 0.1847. > test-miss.R: Distance from origin on tolerance region scale: 3.678483 (previously 39.20962). > test-miss.R: Estimating equations are not within tolerance region. > test-miss.R: > test-miss.R: Iteration 3 of at most 60 with parameter: > test-miss.R: edges > test-miss.R: -3.138904 > test-miss.R: Starting unconstrained MCMC... > test-miss.R: Back from unconstrained MCMC. > test-miss.R: Starting constrained MCMC... > test-miss.R: Back from constrained MCMC. > test-miss.R: New interval = 128. > test-miss.R: New constrained interval = 64. > test-miss.R: Average estimating function values: > test-miss.R: edges > test-miss.R: -0.05349794 > test-miss.R: Starting MCMLE Optimization... > test-miss.R: 1 > test-miss.R: Optimizing with step length 1.0000. > test-miss.R: Using lognormal metric (see control.ergm function). > test-miss.R: Using log-normal approx with missing (no optim) > test-miss.R: Starting MCMC s.e. computation. > test-miss.R: The log-likelihood improved by 0.0008. > test-miss.R: Distance from origin on tolerance region scale: 0.01512904 (previously 4.293514). > test-miss.R: Test statistic: T^2 = 22.84874, with 1 free parameter(s) and 256.603 degrees of freedom. > test-miss.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-miss.R: Finished MCMLE. > test-miss.R: Evaluating log-likelihood at the estimate. > test-miss.R: Initializing model to obtain the list of dyad-independent terms... > test-miss.R: Fitting the dyad-independent submodel... > test-miss.R: Dyad-independent submodel MLE has likelihood -8.355963 at: > test-miss.R: [1] -3.157 0.000 > test-miss.R: Bridging between the dyad-independent submodel and the full model... > test-miss.R: Setting up bridge sampling... > test-miss.R: Initializing model and proposals... > test-miss.R: Model and proposals initialized. > test-miss.R: Initializing constrained model and proposals... > test-miss.R: Constrained model and proposals initialized. > test-miss.R: Using 16 bridges: > test-miss.R: Running theta=[-3.11196, 0.00000]. > test-miss.R: Running theta=[-3.114866, 0.000000]. > test-miss.R: Running theta=[-3.117772, 0.000000]. > test-miss.R: Running theta=[-3.120678, 0.000000]. > test-miss.R: Running theta=[-3.123584, 0.000000]. > test-miss.R: Running theta=[-3.126489, 0.000000]. > test-miss.R: Running theta=[-3.129395, 0.000000]. > test-miss.R: Running theta=[-3.132301, 0.000000]. > test-miss.R: Running theta=[-3.135207, 0.000000]. > test-miss.R: Running theta=[-3.138113, 0.000000]. > test-miss.R: Running theta=[-3.141018, 0.000000]. > test-miss.R: Running theta=[-3.143924, 0.000000]. > test-miss.R: Running theta=[-3.14683, 0.00000]. > test-miss.R: Running theta=[-3.149736, 0.000000]. > test-miss.R: Running theta=[-3.152642, 0.000000]. > test-miss.R: Running theta=[-3.155548, 0.000000]. > test-miss.R: . > test-miss.R: Bridge sampling finished. Collating... > test-miss.R: Bridging finished. > test-miss.R: > test-miss.R: This model was fit using MCMC. To examine model diagnostics and check > test-miss.R: for degeneracy, use the mcmc.diagnostics() function. > test-miss.R: Sample statistics summary: > test-miss.R: > test-miss.R: Iterations = 3584:65536 > test-miss.R: Thinning interval = 256 > test-miss.R: Number of chains = 1 > test-miss.R: Sample size per chain = 243 > test-miss.R: > test-miss.R: 1. Empirical mean and standard deviation for each variable, > test-miss.R: plus standard error of the mean: > test-miss.R: > test-miss.R: Mean SD Naive SE Time-series SE > test-miss.R: -0.05350 1.39509 0.08949 0.08949 > test-miss.R: > test-miss.R: 2. Quantiles for each variable: > test-miss.R: > test-miss.R: 2.5% 25% 50% 75% 97.5% > test-miss.R: -2.05761 -1.05761 -0.05761 0.94239 2.94239 > test-miss.R: > test-miss.R: Constrained sample statistics summary: > test-miss.R: > test-miss.R: Iterations = 1792:32768 > test-miss.R: Thinning interval = 128 > test-miss.R: Number of chains = 1 > test-miss.R: Sample size per chain = 243 > test-miss.R: > test-miss.R: 1. Empirical mean and standard deviation for each variable, > test-miss.R: plus standard error of the mean: > test-miss.R: > test-miss.R: Mean SD Naive SE Time-series SE > test-miss.R: 9.838e-19 2.335e-01 1.498e-02 1.763e-02 > test-miss.R: > test-miss.R: 2. Quantiles for each variable: > test-miss.R: > test-miss.R: 2.5% 25% 50% 75% 97.5% > test-miss.R: -0.05761 -0.05761 -0.05761 -0.05761 0.94239 > test-miss.R: > test-miss.R: > test-miss.R: Are unconstrained sample statistics significantly different from constrained? > test-miss.R: edges (Omni) > test-miss.R: diff. -0.05349794 NA > test-miss.R: test stat. -0.58650248 0.3476007 > test-miss.R: P-val. 0.55753790 0.5559932 > test-miss.R: > test-miss.R: Sample statistics cross-correlations: > test-miss.R: edges > test-miss.R: edges 1 > test-miss.R: Constrained sample statistics cross-correlations: > test-miss.R: edges > test-miss.R: edges 1 > test-miss.R: > test-miss.R: Sample statistics auto-correlation: > test-miss.R: Chain 1 > test-miss.R: edges > test-miss.R: Lag 0 1.00000000 > test-miss.R: Lag 256 -0.07640761 > test-miss.R: Lag 512 0.04674441 > test-miss.R: Lag 768 -0.08703223 > test-miss.R: Lag 1024 -0.01273911 > test-miss.R: Lag 1280 0.08918131 > test-miss.R: Constrained sample statistics auto-correlation: > test-miss.R: Chain 1 > test-miss.R: edges > test-miss.R: Lag 0 1.00000000 > test-miss.R: Lag 128 0.01440843 > test-miss.R: Lag 256 -0.06163854 > test-miss.R: Lag 384 -0.05752332 > test-miss.R: Lag 512 0.09381586 > test-miss.R: Lag 640 0.16935966 > test-miss.R: > test-miss.R: Sample statistics burn-in diagnostic (Geweke): > test-miss.R: Chain 1 > test-miss.R: > test-miss.R: Fraction in 1st window = 0.1 > test-miss.R: Fraction in 2nd window = 0.5 > test-miss.R: > test-miss.R: edges > test-miss.R: 1.222791 > test-miss.R: > test-miss.R: Individual P-values (lower = worse): > test-miss.R: edges > test-miss.R: 0.2214088 > test-miss.R: Joint P-value (lower = worse): 0.530107 > test-miss.R: Sample statistics burn-in diagnostic (Geweke): > test-miss.R: Chain 1 > test-miss.R: > test-miss.R: Fraction in 1st window = 0.1 > test-miss.R: Fraction in 2nd window = 0.5 > test-miss.R: > test-miss.R: edges > test-miss.R: 0.899493 > test-miss.R: > test-miss.R: P-values (lower = worse): > test-miss.R: edges > test-miss.R: 0.3683901 > test-miss.R: Joint P-value (lower = worse): 0.530107 . > test-miss.R: > test-miss.R: Note: MCMC diagnostics shown here are from the last round of > test-miss.R: simulation, prior to computation of final parameter estimates. > test-miss.R: Because the final estimates are refinements of those used for this > test-miss.R: simulation run, these diagnostics may understate model performance. > test-miss.R: To directly assess the performance of the final model on in-model > test-miss.R: statistics, please use the GOF command: gof(ergmFitObject, > test-miss.R: GOF=~model). > test-miss.R: > test-miss.R: MCMCMLE estimate = -3.110507 with log-likelihood -8.357166 OK. > test-miss.R: Network statistics: > test-miss.R: edges esp#1 esp#2 esp#3 esp#4 esp#5 esp#6 esp#7 esp#8 esp#9 esp#10 > test-miss.R: 50 24 3 0 0 0 0 0 0 0 0 > test-miss.R: esp#11 esp#12 esp#13 esp#14 esp#15 esp#16 esp#17 esp#18 esp#19 esp#20 esp#21 > test-miss.R: 0 0 0 0 0 0 0 0 0 0 0 > test-miss.R: esp#22 esp#23 esp#24 esp#25 esp#26 esp#27 esp#28 > test-miss.R: 0 0 0 0 0 0 0 > test-miss.R: Correct estimate = -2.028148 > test-miss.R: Starting maximum pseudolikelihood estimation (MPLE): > test-miss.R: Obtaining the responsible dyads. > test-miss.R: Evaluating the predictor and response matrix. > test-miss.R: Maximizing the pseudolikelihood. > test-miss.R: Finished MPLE. > test-miss.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-miss.R: Iteration 1 of at most 5: > test-miss.R: 1 > test-miss.R: 2 > test-miss.R: 3 > test-miss.R: 4 > test-miss.R: 5 > test-miss.R: 6 > test-miss.R: 7 > test-miss.R: 8 > test-miss.R: 9 > test-miss.R: 10 > test-miss.R: 11 > test-miss.R: Optimizing with step length 1.0000. > test-miss.R: The log-likelihood improved by < 0.0001. > test-miss.R: Estimating equations are not within tolerance region. > test-miss.R: Iteration 2 of at most 5: > test-miss.R: 1 > test-miss.R: 2 > test-miss.R: 3 > test-miss.R: 4 > test-miss.R: 5 > test-miss.R: 6 > test-miss.R: 7 > test-miss.R: 8 9 > test-miss.R: Optimizing with step length 1.0000. > test-miss.R: The log-likelihood improved by < 0.0001. > test-miss.R: Estimating equations are not within tolerance region. > test-miss.R: Iteration 3 of at most 5: > test-mple-cov.R: Finished MPLE. > test-mple-cov.R: Evaluating log-likelihood at the estimate. > test-mple-cov.R: > test-mple-cov.R: Starting maximum pseudolikelihood estimation (MPLE): > test-mple-cov.R: Obtaining the responsible dyads. > test-mple-cov.R: Evaluating the predictor and response matrix. > test-mple-cov.R: Maximizing the pseudolikelihood. > test-miss.R: 1 > test-miss.R: 2 > test-miss.R: 3 > test-miss.R: 4 > test-miss.R: 5 > test-miss.R: 6 > test-miss.R: 7 > test-miss.R: 8 > test-miss.R: 9 10 > test-miss.R: 11 > test-miss.R: Optimizing with step length 1.0000. > test-miss.R: The log-likelihood improved by < 0.0001. > test-miss.R: Estimating equations are not within tolerance region. > test-miss.R: Iteration 4 of at most 5: > test-miss.R: 1 > test-miss.R: 2 > test-miss.R: 3 > test-miss.R: 4 > test-miss.R: 5 > test-miss.R: 6 > test-miss.R: 7 > test-miss.R: 8 > test-miss.R: Optimizing with step length 1.0000. > test-miss.R: The log-likelihood improved by < 0.0001. > test-miss.R: Estimating equations are not within tolerance region. > test-miss.R: Iteration 5 of at most 5: > test-mple-cov.R: Estimating Godambe Matrix using 500 simulated networks. > test-miss.R: 1 > test-miss.R: 2 > test-miss.R: 3 > test-miss.R: 4 > test-miss.R: 5 > test-miss.R: 6 > test-miss.R: 7 8 9 Optimizing with step length 1.0000. > test-miss.R: The log-likelihood improved by < 0.0001. > test-miss.R: Estimating equations are not within tolerance region. > test-miss.R: Estimating equations did not move closer to tolerance region more than 1 time(s) in 4 steps; increasing sample size. > test-miss.R: MCMLE estimation did not converge after 5 iterations. The estimated coefficients may not be accurate. Estimation may be resumed by passing the coefficients as initial values; see 'init' under ?control.ergm for details. > test-miss.R: Finished MCMLE. > test-miss.R: Evaluating log-likelihood at the estimate. > test-miss.R: Fitting the dyad-independent submodel... > test-miss.R: Bridging between the dyad-independent submodel and the full model... > test-miss.R: Setting up bridge sampling... > test-miss.R: Using 16 bridges: 1 > test-miss.R: 2 > test-miss.R: 3 > test-miss.R: 4 > test-miss.R: 5 > test-miss.R: 6 > test-miss.R: 7 > test-miss.R: 8 > test-miss.R: 9 > test-miss.R: 10 > test-miss.R: 11 > test-miss.R: 12 > test-miss.R: 13 > test-miss.R: 14 > test-miss.R: 15 > test-miss.R: 16 > test-miss.R: . > test-miss.R: Bridging finished. > test-miss.R: > test-miss.R: This model was fit using MCMC. To examine model diagnostics and check > test-miss.R: for degeneracy, use the mcmc.diagnostics() function. > test-mple-largenetwork.R: Starting maximum pseudolikelihood estimation (MPLE): > test-mple-largenetwork.R: Obtaining the responsible dyads. > test-mple-largenetwork.R: Evaluating the predictor and response matrix. > test-mple-largenetwork.R: Maximizing the pseudolikelihood. > test-mple-largenetwork.R: Finished MPLE. > test-mple-largenetwork.R: Evaluating log-likelihood at the estimate. > test-mple-largenetwork.R: > test-mple-largenetwork.R: Starting maximum pseudolikelihood estimation (MPLE): > test-mple-largenetwork.R: Obtaining the responsible dyads. > test-mple-largenetwork.R: Evaluating the predictor and response matrix. > test-mple-largenetwork.R: Maximizing the pseudolikelihood. > test-mple-largenetwork.R: Finished MPLE. > test-mple-largenetwork.R: Evaluating log-likelihood at the estimate. > test-mple-largenetwork.R: > test-mple-offset.R: Starting maximum pseudolikelihood estimation (MPLE): > test-mple-offset.R: Obtaining the responsible dyads. > test-mple-offset.R: Evaluating the predictor and response matrix. > test-mple-offset.R: Maximizing the pseudolikelihood. > test-mple-offset.R: Finished MPLE. > test-mple-offset.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-mple-offset.R: Iteration 1 of at most 60: > test-mple-offset.R: 1 > test-mple-offset.R: Optimizing with step length 1.0000. > test-mple-offset.R: The log-likelihood improved by 0.0040. > test-mple-offset.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-mple-offset.R: Finished MCMLE. > test-mple-offset.R: This model was fit using MCMC. To examine model diagnostics and check > test-mple-offset.R: for degeneracy, use the mcmc.diagnostics() function. > test-mple-target.R: [1] 350 50 250 > test-mple-target.R: Structural check: > test-mple-target.R: Mean degree: 1.4 . > test-mple-target.R: Average degree among nodes with degree 2 or higher: 2.25 . > test-mple-target.R: Starting maximum pseudolikelihood estimation (MPLE): > test-mple-target.R: Obtaining the responsible dyads. > test-mple-target.R: Evaluating the predictor and response matrix. > test-mple-target.R: Maximizing the pseudolikelihood. > test-mple-target.R: Finished MPLE. > test-mple-target.R: Starting maximum pseudolikelihood estimation (MPLE): > test-mple-target.R: Obtaining the responsible dyads. > test-mple-target.R: Evaluating the predictor and response matrix. > test-mple-target.R: Maximizing the pseudolikelihood. > test-mple-target.R: Finished MPLE. > test-mple-target.R: Starting maximum pseudolikelihood estimation (MPLE): > test-mple-target.R: Obtaining the responsible dyads. > test-mple-target.R: Evaluating the predictor and response matrix. > test-mple-target.R: Maximizing the pseudolikelihood. > test-mple-target.R: Finished MPLE. > test-mple-target.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-mple-target.R: Iteration 1 of at most 60: > test-networkLite.R: Loading required package: networkLite > test-mple-cov.R: Finished MPLE. > test-mple-cov.R: Evaluating log-likelihood at the estimate. > test-mple-cov.R: > test-mple-cov.R: Starting maximum pseudolikelihood estimation (MPLE): > test-mple-cov.R: Obtaining the responsible dyads. > test-mple-cov.R: Evaluating the predictor and response matrix. > test-mple-cov.R: Maximizing the pseudolikelihood. > test-mple-cov.R: Finished MPLE. > test-mple-cov.R: Evaluating log-likelihood at the estimate. > test-mple-cov.R: > test-mple-cov.R: Starting maximum pseudolikelihood estimation (MPLE): > test-mple-cov.R: Obtaining the responsible dyads. > test-mple-cov.R: Evaluating the predictor and response matrix. > test-mple-cov.R: Maximizing the pseudolikelihood. > test-mple-cov.R: Estimating Bootstrap Standard Errors using 500 simulated networks. > test-mple-cov.R: Finished MPLE. > test-mple-cov.R: Evaluating log-likelihood at the estimate. > test-mple-cov.R: > test-mple-cov.R: Starting maximum pseudolikelihood estimation (MPLE): > test-mple-cov.R: Obtaining the responsible dyads. > test-mple-cov.R: Evaluating the predictor and response matrix. > test-mple-cov.R: Maximizing the pseudolikelihood. > test-mple-cov.R: Finished MPLE. > test-mple-cov.R: Evaluating log-likelihood at the estimate. > test-mple-cov.R: > test-networkLite.R: Starting maximum pseudolikelihood estimation (MPLE): > test-networkLite.R: Obtaining the responsible dyads. > test-networkLite.R: Evaluating the predictor and response matrix. > test-networkLite.R: Maximizing the pseudolikelihood. > test-networkLite.R: Finished MPLE. > test-networkLite.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-networkLite.R: Iteration 1 of at most 60: > test-networkLite.R: 1 > test-networkLite.R: Optimizing with step length 1.0000. > test-networkLite.R: The log-likelihood improved by 0.0297. > test-networkLite.R: Convergence test p-value: 0.0001. Converged with 99% confidence. > test-networkLite.R: Finished MCMLE. > test-networkLite.R: This model was fit using MCMC. To examine model diagnostics and check > test-networkLite.R: for degeneracy, use the mcmc.diagnostics() function. > test-networkLite.R: Starting maximum pseudolikelihood estimation (MPLE): > test-networkLite.R: Obtaining the responsible dyads. > test-networkLite.R: Evaluating the predictor and response matrix. > test-networkLite.R: Maximizing the pseudolikelihood. > test-networkLite.R: Finished MPLE. > test-networkLite.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-networkLite.R: Iteration 1 of at most 60: > test-networkLite.R: 1 > test-networkLite.R: 2 > test-networkLite.R: 3 > test-networkLite.R: 4 > test-networkLite.R: 5 > test-networkLite.R: 6 > test-networkLite.R: 7 > test-networkLite.R: 8 > test-networkLite.R: 9 > test-networkLite.R: 10 > test-networkLite.R: 11 > test-networkLite.R: 12 > test-networkLite.R: Optimizing with step length 1.0000. > test-networkLite.R: The log-likelihood improved by 0.1793. > test-networkLite.R: Estimating equations are not within tolerance region. > test-networkLite.R: Iteration 2 of at most 60: > test-networkLite.R: 1 > test-networkLite.R: 2 > test-networkLite.R: 3 > test-networkLite.R: 4 > test-networkLite.R: 5 > test-networkLite.R: 6 > test-networkLite.R: 7 > test-networkLite.R: 8 > test-networkLite.R: 9 > test-networkLite.R: 10 > test-networkLite.R: 11 > test-networkLite.R: 12 > test-networkLite.R: Optimizing with step length 1.0000. > test-networkLite.R: The log-likelihood improved by 0.0302. > test-networkLite.R: Convergence test p-value: 0.0036. Converged with 99% confidence. > test-networkLite.R: Finished MCMLE. > test-networkLite.R: This model was fit using MCMC. To examine model diagnostics and check > test-networkLite.R: for degeneracy, use the mcmc.diagnostics() function. > test-networkLite.R: Starting maximum pseudolikelihood estimation (MPLE): > test-networkLite.R: Obtaining the responsible dyads. > test-networkLite.R: Evaluating the predictor and response matrix. > test-networkLite.R: Maximizing the pseudolikelihood. > test-networkLite.R: Finished MPLE. > test-networkLite.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-networkLite.R: Iteration 1 of at most 60: > test-networkLite.R: 1 > test-networkLite.R: Optimizing with step length 1.0000. > test-networkLite.R: The log-likelihood improved by 0.0297. > test-networkLite.R: Convergence test p-value: 0.0001. Converged with 99% confidence. > test-networkLite.R: Finished MCMLE. > test-networkLite.R: This model was fit using MCMC. To examine model diagnostics and check > test-networkLite.R: for degeneracy, use the mcmc.diagnostics() function. > test-networkLite.R: Starting maximum pseudolikelihood estimation (MPLE): > test-networkLite.R: Obtaining the responsible dyads. > test-networkLite.R: Evaluating the predictor and response matrix. > test-networkLite.R: Maximizing the pseudolikelihood. > test-networkLite.R: Finished MPLE. > test-networkLite.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-networkLite.R: Iteration 1 of at most 60: > test-networkLite.R: 1 2 > test-networkLite.R: 3 > test-networkLite.R: 4 > test-networkLite.R: 5 > test-networkLite.R: 6 > test-networkLite.R: 7 > test-networkLite.R: 8 > test-networkLite.R: 9 > test-networkLite.R: 10 > test-networkLite.R: 11 > test-networkLite.R: 12 > test-networkLite.R: Optimizing with step length 1.0000. > test-networkLite.R: The log-likelihood improved by 0.1793. > test-networkLite.R: Estimating equations are not within tolerance region. > test-networkLite.R: Iteration 2 of at most 60: > test-networkLite.R: 1 > test-networkLite.R: 2 > test-networkLite.R: 3 > test-networkLite.R: 4 > test-networkLite.R: 5 > test-networkLite.R: 6 > test-networkLite.R: 7 > test-networkLite.R: 8 > test-networkLite.R: 9 > test-networkLite.R: 10 > test-networkLite.R: 11 > test-networkLite.R: 12 > test-networkLite.R: Optimizing with step length 1.0000. > test-networkLite.R: The log-likelihood improved by 0.0302. > test-networkLite.R: Convergence test p-value: 0.0036. Converged with 99% confidence. > test-networkLite.R: Finished MCMLE. > test-networkLite.R: This model was fit using MCMC. To examine model diagnostics and check > test-networkLite.R: for degeneracy, use the mcmc.diagnostics() function. > test-networkLite.R: Starting maximum pseudolikelihood estimation (MPLE): > test-networkLite.R: Obtaining the responsible dyads. > test-networkLite.R: Evaluating the predictor and response matrix. > test-networkLite.R: Maximizing the pseudolikelihood. > test-networkLite.R: Finished MPLE. > test-networkLite.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-networkLite.R: Iteration 1 of at most 60: > test-networkLite.R: 1 > test-networkLite.R: Optimizing with step length 1.0000. > test-networkLite.R: The log-likelihood improved by 0.1592. > test-networkLite.R: Estimating equations are not within tolerance region. > test-networkLite.R: Iteration 2 of at most 60: > test-networkLite.R: 1 > test-networkLite.R: Optimizing with step length 1.0000. > test-networkLite.R: The log-likelihood improved by 0.0101. > test-networkLite.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-networkLite.R: Finished MCMLE. > test-networkLite.R: This model was fit using MCMC. To examine model diagnostics and check > test-networkLite.R: for degeneracy, use the mcmc.diagnostics() function. > test-networkLite.R: Starting maximum pseudolikelihood estimation (MPLE): > test-networkLite.R: Obtaining the responsible dyads. > test-networkLite.R: Evaluating the predictor and response matrix. > test-networkLite.R: Maximizing the pseudolikelihood. > test-networkLite.R: Finished MPLE. > test-networkLite.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-networkLite.R: Iteration 1 of at most 60: > test-networkLite.R: 1 2 > test-networkLite.R: 3 > test-networkLite.R: 4 > test-networkLite.R: 5 > test-networkLite.R: 6 7 > test-networkLite.R: 8 > test-networkLite.R: 9 > test-networkLite.R: 10 > test-networkLite.R: 11 > test-networkLite.R: Optimizing with step length 1.0000. > test-networkLite.R: The log-likelihood improved by 0.0392. > test-networkLite.R: Convergence test p-value: 0.0019. Converged with 99% confidence. > test-networkLite.R: Finished MCMLE. > test-networkLite.R: This model was fit using MCMC. To examine model diagnostics and check > test-networkLite.R: for degeneracy, use the mcmc.diagnostics() function. > test-networkLite.R: Starting maximum pseudolikelihood estimation (MPLE): > test-networkLite.R: Obtaining the responsible dyads. > test-networkLite.R: Evaluating the predictor and response matrix. > test-networkLite.R: Maximizing the pseudolikelihood. > test-networkLite.R: Finished MPLE. > test-networkLite.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-networkLite.R: Iteration 1 of at most 60: > test-networkLite.R: 1 > test-networkLite.R: Optimizing with step length 1.0000. > test-networkLite.R: The log-likelihood improved by 0.1592. > test-networkLite.R: Estimating equations are not within tolerance region. > test-networkLite.R: Iteration 2 of at most 60: > test-networkLite.R: 1 > test-networkLite.R: Optimizing with step length 1.0000. > test-networkLite.R: The log-likelihood improved by 0.0101. > test-networkLite.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-networkLite.R: Finished MCMLE. > test-networkLite.R: This model was fit using MCMC. To examine model diagnostics and check > test-networkLite.R: for degeneracy, use the mcmc.diagnostics() function. > test-networkLite.R: Starting maximum pseudolikelihood estimation (MPLE): > test-networkLite.R: Obtaining the responsible dyads. > test-networkLite.R: Evaluating the predictor and response matrix. > test-networkLite.R: Maximizing the pseudolikelihood. > test-networkLite.R: Finished MPLE. > test-networkLite.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-networkLite.R: Iteration 1 of at most 60: > test-networkLite.R: 1 2 > test-networkLite.R: 3 > test-networkLite.R: 4 > test-networkLite.R: 5 > test-networkLite.R: 6 > test-networkLite.R: 7 > test-networkLite.R: 8 > test-networkLite.R: 9 10 11 Optimizing with step length 1.0000. > test-networkLite.R: The log-likelihood improved by 0.0392. > test-networkLite.R: Convergence test p-value: 0.0019. Converged with 99% confidence. > test-networkLite.R: Finished MCMLE. > test-networkLite.R: This model was fit using MCMC. To examine model diagnostics and check > test-networkLite.R: for degeneracy, use the mcmc.diagnostics() function. > test-networkLite.R: Starting maximum pseudolikelihood estimation (MPLE): > test-networkLite.R: Obtaining the responsible dyads. > test-networkLite.R: Evaluating the predictor and response matrix. > test-networkLite.R: Maximizing the pseudolikelihood. > test-networkLite.R: Finished MPLE. > test-networkLite.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-networkLite.R: Iteration 1 of at most 60: > test-networkLite.R: 1 > test-networkLite.R: Optimizing with step length 0.9410. > test-networkLite.R: The log-likelihood improved by 5.9387. > test-networkLite.R: Estimating equations are not within tolerance region. > test-networkLite.R: Iteration 2 of at most 60: > test-networkLite.R: 1 Optimizing with step length 1.0000. > test-networkLite.R: The log-likelihood improved by 1.8905. > test-networkLite.R: Estimating equations are not within tolerance region. > test-networkLite.R: Iteration 3 of at most 60: > test-networkLite.R: 1 > test-networkLite.R: Optimizing with step length 1.0000. > test-networkLite.R: The log-likelihood improved by 0.1368. > test-networkLite.R: Estimating equations are not within tolerance region. > test-networkLite.R: Iteration 4 of at most 60: > test-nodrop.R: Starting maximum pseudolikelihood estimation (MPLE): > test-nodrop.R: Obtaining the responsible dyads. > test-nodrop.R: Evaluating the predictor and response matrix. > test-nodrop.R: Maximizing the pseudolikelihood. > test-nodrop.R: Finished MPLE. > test-nodrop.R: Starting maximum pseudolikelihood estimation (MPLE): > test-nodrop.R: Obtaining the responsible dyads. > test-nodrop.R: Evaluating the predictor and response matrix. > test-nodrop.R: Maximizing the pseudolikelihood. > test-nodrop.R: Finished MPLE. > test-nodrop.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-nodrop.R: Iteration 1 of at most 2: > test-networkLite.R: 1 Optimizing with step length 1.0000. > test-networkLite.R: The log-likelihood improved by 0.0176. > test-networkLite.R: Convergence test p-value: 0.0247. > test-networkLite.R: Not converged with 99% confidence; increasing sample size. > test-networkLite.R: Iteration 5 of at most 60: > test-nodrop.R: 1 > test-nodrop.R: Optimizing with step length 1.0000. > test-nodrop.R: The log-likelihood improved by 0.0005. > test-nodrop.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-nodrop.R: Finished MCMLE. > test-nodrop.R: This model was fit using MCMC. To examine model diagnostics and check > test-nodrop.R: for degeneracy, use the mcmc.diagnostics() function. > test-nodrop.R: Starting maximum pseudolikelihood estimation (MPLE): > test-nodrop.R: Obtaining the responsible dyads. > test-nodrop.R: Evaluating the predictor and response matrix. > test-nodrop.R: Maximizing the pseudolikelihood. > test-nodrop.R: Finished MPLE. > test-networkLite.R: 1 > test-networkLite.R: Optimizing with step length 1.0000. > test-nodrop.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-nodrop.R: Iteration 1 of at most 2: > test-networkLite.R: The log-likelihood improved by 0.0103. > test-networkLite.R: Convergence test p-value: 0.0227. Not converged with 99% confidence; increasing sample size. > test-networkLite.R: Iteration 6 of at most 60: > test-nodrop.R: 1 Optimizing with step length 0.2247. > test-nodrop.R: The log-likelihood improved by 2.2822. > test-nodrop.R: Estimating equations are not within tolerance region. > test-nodrop.R: Iteration 2 of at most 2: > test-nodrop.R: 1 Optimizing with step length 0.2817. > test-nodrop.R: The log-likelihood improved by 2.4734. > test-nodrop.R: Estimating equations are not within tolerance region. > test-nodrop.R: MCMLE estimation did not converge after 2 iterations. The estimated coefficients may not be accurate. Estimation may be resumed by passing the coefficients as initial values; see 'init' under ?control.ergm for details. > test-nodrop.R: Finished MCMLE. > test-nodrop.R: This model was fit using MCMC. To examine model diagnostics and check > test-nodrop.R: for degeneracy, use the mcmc.diagnostics() function. > test-nodrop.R: Starting maximum pseudolikelihood estimation (MPLE): > test-nodrop.R: Obtaining the responsible dyads. > test-nodrop.R: Evaluating the predictor and response matrix. > test-nodrop.R: Maximizing the pseudolikelihood. > test-nodrop.R: Finished MPLE. > test-networkLite.R: 1 > test-networkLite.R: Optimizing with step length 1.0000. > test-nodrop.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-nodrop.R: Iteration 1 of at most 2: > test-networkLite.R: The log-likelihood improved by 0.0419. > test-networkLite.R: Convergence test p-value: 0.0085. > test-networkLite.R: Converged with 99% confidence. > test-networkLite.R: Finished MCMLE. > test-networkLite.R: This model was fit using MCMC. To examine model diagnostics and check > test-networkLite.R: for degeneracy, use the mcmc.diagnostics() function. > test-nodrop.R: 1 > test-nodrop.R: Optimizing with step length 0.2675. > test-nodrop.R: The log-likelihood improved by 3.3752. > test-nodrop.R: Estimating equations are not within tolerance region. > test-nodrop.R: Iteration 2 of at most 2: > test-networkLite.R: Starting maximum pseudolikelihood estimation (MPLE): > test-networkLite.R: Obtaining the responsible dyads. > test-networkLite.R: Evaluating the predictor and response matrix. > test-networkLite.R: Maximizing the pseudolikelihood. > test-networkLite.R: Finished MPLE. > test-networkLite.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-networkLite.R: Iteration 1 of at most 60: > test-nodrop.R: 1 > test-nodrop.R: Optimizing with step length 0.3098. > test-nodrop.R: The log-likelihood improved by 2.4783. > test-nodrop.R: Estimating equations are not within tolerance region. > test-nodrop.R: MCMLE estimation did not converge after 2 iterations. The estimated coefficients may not be accurate. Estimation may be resumed by passing the coefficients as initial values; see 'init' under ?control.ergm for details. > test-nodrop.R: Finished MCMLE. > test-nodrop.R: This model was fit using MCMC. To examine model diagnostics and check > test-nodrop.R: for degeneracy, use the mcmc.diagnostics() function. > test-nonident-test.R: Starting maximum pseudolikelihood estimation (MPLE): > test-nonident-test.R: Obtaining the responsible dyads. > test-nonident-test.R: Evaluating the predictor and response matrix. > test-nonident-test.R: Maximizing the pseudolikelihood. > test-nonident-test.R: Finished MPLE. > test-networkLite.R: 1 > test-networkLite.R: 2 > test-networkLite.R: 3 > test-networkLite.R: 4 > test-networkLite.R: 5 > test-networkLite.R: 6 7 > test-networkLite.R: 8 > test-networkLite.R: 9 > test-networkLite.R: 10 > test-networkLite.R: 11 > test-networkLite.R: 12 > test-networkLite.R: 13 > test-networkLite.R: 14 > test-networkLite.R: 15 16 17 18 19 20 21 22 23 Optimizing with step length 1.0000. > test-networkLite.R: The log-likelihood improved by 0.1680. > test-networkLite.R: Estimating equations are not within tolerance region. > test-networkLite.R: Iteration 2 of at most 60: > test-nonident-test.R: Starting maximum pseudolikelihood estimation (MPLE): > test-nonident-test.R: Obtaining the responsible dyads. > test-nonident-test.R: Evaluating the predictor and response matrix. > test-nonident-test.R: Maximizing the pseudolikelihood. > test-nonident-test.R: Finished MPLE. > test-nonident-test.R: Starting maximum pseudolikelihood estimation (MPLE): > test-nonident-test.R: Obtaining the responsible dyads. > test-nonident-test.R: Evaluating the predictor and response matrix. > test-nonident-test.R: Maximizing the pseudolikelihood. > test-nonident-test.R: Finished MPLE. > test-nonident-test.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-nonident-test.R: Iteration 1 of at most 1: > test-networkLite.R: 1 > test-networkLite.R: 2 > test-networkLite.R: 3 > test-networkLite.R: 4 > test-networkLite.R: 5 > test-networkLite.R: 6 > test-networkLite.R: 7 > test-networkLite.R: 8 > test-networkLite.R: 9 > test-networkLite.R: 10 > test-networkLite.R: 11 > test-networkLite.R: Optimizing with step length 1.0000. > test-networkLite.R: The log-likelihood improved by 0.0094. > test-networkLite.R: Convergence test p-value: 0.0010. Converged with 99% confidence. > test-networkLite.R: Finished MCMLE. > test-networkLite.R: This model was fit using MCMC. To examine model diagnostics and check > test-networkLite.R: for degeneracy, use the mcmc.diagnostics() function. > test-nonident-test.R: 1 > test-nonident-test.R: Optimizing with step length 1.0000. > test-networkLite.R: Starting maximum pseudolikelihood estimation (MPLE): > test-networkLite.R: Obtaining the responsible dyads. > test-networkLite.R: Evaluating the predictor and response matrix. > test-networkLite.R: Maximizing the pseudolikelihood. > test-networkLite.R: Finished MPLE. > test-networkLite.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-nonident-test.R: The log-likelihood improved by < 0.0001. > test-nonident-test.R: Estimating equations are not within tolerance region. > test-nonident-test.R: MCMLE estimation did not converge after 1 iterations. The estimated coefficients may not be accurate. Estimation may be resumed by passing the coefficients as initial values; see 'init' under ?control.ergm for details. > test-nonident-test.R: Finished MCMLE. > test-nonident-test.R: This model was fit using MCMC. To examine model diagnostics and check > test-nonident-test.R: for degeneracy, use the mcmc.diagnostics() function. > test-networkLite.R: Iteration 1 of at most 60: > test-nonident-test.R: Starting maximum pseudolikelihood estimation (MPLE): > test-nonident-test.R: Obtaining the responsible dyads. > test-nonident-test.R: Evaluating the predictor and response matrix. > test-nonident-test.R: Maximizing the pseudolikelihood. > test-nonident-test.R: Finished MPLE. > test-nonident-test.R: Starting maximum pseudolikelihood estimation (MPLE): > test-nonident-test.R: Obtaining the responsible dyads. > test-nonident-test.R: Evaluating the predictor and response matrix. > test-nonident-test.R: Maximizing the pseudolikelihood. > test-nonident-test.R: Finished MPLE. > test-networkLite.R: 1 > test-networkLite.R: Optimizing with step length 0.9410. > test-nonident-test.R: Starting maximum pseudolikelihood estimation (MPLE): > test-nonident-test.R: Obtaining the responsible dyads. > test-nonident-test.R: Evaluating the predictor and response matrix. > test-nonident-test.R: Maximizing the pseudolikelihood. > test-nonident-test.R: Finished MPLE. > test-networkLite.R: The log-likelihood improved by 5.9387. > test-networkLite.R: Estimating equations are not within tolerance region. > test-networkLite.R: Iteration 2 of at most 60: > test-nonident-test.R: Starting maximum pseudolikelihood estimation (MPLE): > test-nonident-test.R: Obtaining the responsible dyads. > test-nonident-test.R: Evaluating the predictor and response matrix. > test-nonident-test.R: Maximizing the pseudolikelihood. > test-nonident-test.R: Finished MPLE. > test-nonident-test.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-nonident-test.R: Iteration 1 of at most 1: > test-networkLite.R: 1 > test-networkLite.R: Optimizing with step length 1.0000. > test-networkLite.R: The log-likelihood improved by 1.8905. > test-networkLite.R: Estimating equations are not within tolerance region. > test-networkLite.R: Iteration 3 of at most 60: > test-networkLite.R: 1 > test-networkLite.R: Optimizing with step length 1.0000. > test-networkLite.R: The log-likelihood improved by 0.1368. > test-networkLite.R: Estimating equations are not within tolerance region. > test-networkLite.R: Iteration 4 of at most 60: > test-nonident-test.R: 1 > test-nonident-test.R: Optimizing with step length 0.8147. > test-nonident-test.R: The log-likelihood improved by < 0.0001. > test-nonident-test.R: Estimating equations are not within tolerance region. > test-nonident-test.R: MCMLE estimation did not converge after 1 iterations. The estimated coefficients may not be accurate. Estimation may be resumed by passing the coefficients as initial values; see 'init' under ?control.ergm for details. > test-nonident-test.R: Finished MCMLE. > test-nonident-test.R: This model was fit using MCMC. To examine model diagnostics and check > test-nonident-test.R: for degeneracy, use the mcmc.diagnostics() function. > test-networkLite.R: 1 > test-networkLite.R: Optimizing with step length 1.0000. > test-networkLite.R: The log-likelihood improved by 0.0176. > test-nonident-test.R: Starting maximum pseudolikelihood estimation (MPLE): > test-nonident-test.R: Obtaining the responsible dyads. > test-nonident-test.R: Evaluating the predictor and response matrix. > test-networkLite.R: Convergence test p-value: 0.0247. Not converged with 99% confidence; increasing sample size. > test-networkLite.R: Iteration 5 of at most 60: > test-nonident-test.R: Maximizing the pseudolikelihood. > test-nonident-test.R: Finished MPLE. > test-nonident-test.R: Starting maximum pseudolikelihood estimation (MPLE): > test-nonident-test.R: Obtaining the responsible dyads. > test-nonident-test.R: Evaluating the predictor and response matrix. > test-nonident-test.R: Maximizing the pseudolikelihood. > test-nonident-test.R: Finished MPLE. > test-networkLite.R: 1 > test-networkLite.R: Optimizing with step length 1.0000. > test-networkLite.R: The log-likelihood improved by 0.0103. > test-nonunique-names.R: Starting maximum pseudolikelihood estimation (MPLE): > test-nonunique-names.R: Obtaining the responsible dyads. > test-nonunique-names.R: Evaluating the predictor and response matrix. > test-nonunique-names.R: Maximizing the pseudolikelihood. > test-nonunique-names.R: Finished MPLE. > test-nonunique-names.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-nonunique-names.R: Iteration 1 of at most 1: > test-networkLite.R: Convergence test p-value: 0.0227. Not converged with 99% confidence; increasing sample size. > test-networkLite.R: Iteration 6 of at most 60: > test-networkLite.R: 1 > test-networkLite.R: Optimizing with step length 1.0000. > test-networkLite.R: The log-likelihood improved by 0.0419. > test-networkLite.R: Convergence test p-value: 0.0085. > test-networkLite.R: Converged with 99% confidence. > test-networkLite.R: Finished MCMLE. > test-networkLite.R: This model was fit using MCMC. To examine model diagnostics and check > test-networkLite.R: for degeneracy, use the mcmc.diagnostics() function. > test-networkLite.R: Starting maximum pseudolikelihood estimation (MPLE): > test-networkLite.R: Obtaining the responsible dyads. > test-networkLite.R: Evaluating the predictor and response matrix. > test-networkLite.R: Maximizing the pseudolikelihood. > test-networkLite.R: Finished MPLE. > test-networkLite.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-networkLite.R: Iteration 1 of at most 60: > test-networkLite.R: 1 2 > test-networkLite.R: 3 > test-networkLite.R: 4 > test-networkLite.R: 5 > test-networkLite.R: 6 > test-networkLite.R: 7 > test-networkLite.R: 8 > test-networkLite.R: 9 > test-networkLite.R: 10 > test-networkLite.R: 11 > test-networkLite.R: 12 > test-networkLite.R: 13 > test-networkLite.R: 14 > test-networkLite.R: 15 > test-networkLite.R: 16 > test-networkLite.R: 17 > test-networkLite.R: 18 > test-networkLite.R: 19 > test-networkLite.R: 20 > test-networkLite.R: 21 > test-networkLite.R: 22 > test-networkLite.R: 23 > test-networkLite.R: Optimizing with step length 1.0000. > test-networkLite.R: The log-likelihood improved by 0.1680. > test-networkLite.R: Estimating equations are not within tolerance region. > test-networkLite.R: Iteration 2 of at most 60: > test-networkLite.R: 1 > test-networkLite.R: 2 > test-networkLite.R: 3 > test-networkLite.R: 4 > test-networkLite.R: 5 > test-networkLite.R: 6 > test-networkLite.R: 7 > test-networkLite.R: 8 > test-networkLite.R: 9 > test-networkLite.R: 10 11 > test-networkLite.R: Optimizing with step length 1.0000. > test-nonunique-names.R: 1 > test-nonunique-names.R: Optimizing with step length 1.0000. > test-nonunique-names.R: The log-likelihood improved by 0.0084. > test-networkLite.R: The log-likelihood improved by 0.0094. > test-nonunique-names.R: Convergence test p-value: 0.0480. Not converged with 99% confidence; increasing sample size. > test-nonunique-names.R: MCMLE estimation did not converge after 1 iterations. The estimated coefficients may not be accurate. Estimation may be resumed by passing the coefficients as initial values; see 'init' under ?control.ergm for details. > test-nonunique-names.R: Finished MCMLE. > test-nonunique-names.R: This model was fit using MCMC. To examine model diagnostics and check > test-nonunique-names.R: for degeneracy, use the mcmc.diagnostics() function. > test-networkLite.R: Convergence test p-value: 0.0010. Converged with 99% confidence. > test-networkLite.R: Finished MCMLE. > test-networkLite.R: This model was fit using MCMC. To examine model diagnostics and check > test-networkLite.R: for degeneracy, use the mcmc.diagnostics() function. > test-nonunique-names.R: Sample statistics summary: > test-nonunique-names.R: > test-nonunique-names.R: Iterations = 2304:44032 > test-nonunique-names.R: Thinning interval = 128 > test-nonunique-names.R: Number of chains = 1 > test-nonunique-names.R: Sample size per chain = 327 > test-nonunique-names.R: > test-nonunique-names.R: 1. Empirical mean and standard deviation for each variable, > test-nonunique-names.R: plus standard error of the mean: > test-nonunique-names.R: > test-nonunique-names.R: Mean SD Naive SE Time-series SE > test-nonunique-names.R: edgecov.a -0.2171 3.380 0.1869 0.3114 > test-nonunique-names.R: edgecov.a 0.1346 3.565 0.1971 0.4311 > test-nonunique-names.R: > test-nonunique-names.R: 2. Quantiles for each variable: > test-nonunique-names.R: > test-nonunique-names.R: 2.5% 25% 50% 75% 97.5% > test-nonunique-names.R: edgecov.a -7 -2 0 2 6.85 > test-nonunique-names.R: edgecov.a -7 -2 0 3 7.00 > test-nonunique-names.R: > test-nonunique-names.R: > test-nonunique-names.R: Are sample statistics significantly different from observed? > test-networkLite.R: Best valid proposal 'Unif' cannot take into account hint(s) 'sparse' and 'triadic'. > test-networkLite.R: Starting contrastive divergence estimation via CD-MCMLE: > test-networkLite.R: Iteration 1 of at most 60: > test-networkLite.R: Convergence test P-value:1e-01 > test-networkLite.R: 1 > test-nonunique-names.R: edgecov.a edgecov.a (Omni) > test-nonunique-names.R: diff. -0.2171254 0.1345566 NA > test-nonunique-names.R: test stat. -0.6971750 0.3120903 1.2954084 > test-nonunique-names.R: P-val. 0.4856933 0.7549719 0.5307488 > test-nonunique-names.R: > test-nonunique-names.R: Sample statistics cross-correlations: > test-nonunique-names.R: edgecov.a edgecov.a > test-nonunique-names.R: edgecov.a 1.0000000 0.6853513 > test-nonunique-names.R: edgecov.a 0.6853513 1.0000000 > test-nonunique-names.R: > test-nonunique-names.R: Sample statistics auto-correlation: > test-nonunique-names.R: Chain 1 > test-nonunique-names.R: edgecov.a edgecov.a > test-nonunique-names.R: Lag 0 1.000000000 1.00000000 > test-nonunique-names.R: Lag 128 0.592467538 0.65334235 > test-nonunique-names.R: Lag 256 0.298337375 0.41906307 > test-nonunique-names.R: Lag 384 0.082532656 0.22830518 > test-nonunique-names.R: Lag 512 -0.003477022 0.08811653 > test-nonunique-names.R: Lag 640 -0.090771181 0.03701357 > test-nonunique-names.R: > test-nonunique-names.R: Sample statistics burn-in diagnostic (Geweke): > test-networkLite.R: The log-likelihood improved by 0.01081. > test-networkLite.R: Iteration 2 of at most 60: > test-networkLite.R: Convergence test P-value:8.1e-01 > test-networkLite.R: Convergence detected. Stopping. > test-networkLite.R: 1 > test-networkLite.R: The log-likelihood improved by 0.000224. > test-networkLite.R: Finished CD. > test-networkLite.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-networkLite.R: Iteration 1 of at most 60: > test-nonunique-names.R: Chain 1 > test-nonunique-names.R: > test-nonunique-names.R: Fraction in 1st window = 0.1 > test-nonunique-names.R: Fraction in 2nd window = 0.5 > test-nonunique-names.R: > test-nonunique-names.R: edgecov.a edgecov.a > test-nonunique-names.R: -0.41748721 0.04619135 > test-nonunique-names.R: > test-nonunique-names.R: Individual P-values (lower = worse): > test-nonunique-names.R: edgecov.a edgecov.a > test-nonunique-names.R: 0.6763221 0.9631577 > test-nonunique-names.R: Joint P-value (lower = worse): 0.8447246 > test-nonunique-names.R: > test-nonunique-names.R: Note: MCMC diagnostics shown here are from the last round of > test-nonunique-names.R: simulation, prior to computation of final parameter estimates. > test-nonunique-names.R: Because the final estimates are refinements of those used for this > test-nonunique-names.R: simulation run, these diagnostics may understate model performance. > test-nonunique-names.R: To directly assess the performance of the final model on in-model > test-nonunique-names.R: statistics, please use the GOF command: gof(ergmFitObject, > test-nonunique-names.R: GOF=~model). > test-nonunique-names.R: > test-networkLite.R: 1 > test-networkLite.R: Optimizing with step length 1.0000. > test-networkLite.R: The log-likelihood improved by 1.0636. > test-networkLite.R: Estimating equations are not within tolerance region. > test-networkLite.R: Iteration 2 of at most 60: > test-offsets.R: Starting maximum pseudolikelihood estimation (MPLE): > test-offsets.R: Obtaining the responsible dyads. > test-offsets.R: Evaluating the predictor and response matrix. > test-offsets.R: Maximizing the pseudolikelihood. > test-offsets.R: Finished MPLE. > test-offsets.R: Evaluating log-likelihood at the estimate. > test-offsets.R: > test-networkLite.R: 1 > test-networkLite.R: Optimizing with step length 1.0000. > test-networkLite.R: The log-likelihood improved by 0.0078. > test-networkLite.R: Convergence test p-value: 0.0012. Converged with 99% confidence. > test-networkLite.R: Finished MCMLE. > test-networkLite.R: This model was fit using MCMC. To examine model diagnostics and check > test-networkLite.R: for degeneracy, use the mcmc.diagnostics() function. > test-offsets.R: Starting maximum pseudolikelihood estimation (MPLE): > test-offsets.R: Obtaining the responsible dyads. > test-offsets.R: Evaluating the predictor and response matrix. > test-offsets.R: Maximizing the pseudolikelihood. > test-offsets.R: Finished MPLE. > test-offsets.R: Evaluating log-likelihood at the estimate. > test-offsets.R: > test-networkLite.R: Best valid proposal 'Unif' cannot take into account hint(s) 'sparse' and 'triadic'. > test-offsets.R: Starting maximum pseudolikelihood estimation (MPLE): > test-offsets.R: Obtaining the responsible dyads. > test-offsets.R: Evaluating the predictor and response matrix. > test-offsets.R: Maximizing the pseudolikelihood. > test-offsets.R: Finished MPLE. > test-offsets.R: Evaluating log-likelihood at the estimate. > test-offsets.R: > test-offsets.R: Starting maximum pseudolikelihood estimation (MPLE): > test-offsets.R: Obtaining the responsible dyads. > test-offsets.R: Evaluating the predictor and response matrix. > test-offsets.R: Maximizing the pseudolikelihood. > test-offsets.R: Finished MPLE. > test-offsets.R: Evaluating log-likelihood at the estimate. > test-offsets.R: > test-networkLite.R: Best valid proposal 'Unif' cannot take into account hint(s) 'sparse' and 'triadic'. > test-offsets.R: Starting maximum pseudolikelihood estimation (MPLE): > test-offsets.R: Obtaining the responsible dyads. > test-offsets.R: Evaluating the predictor and response matrix. > test-offsets.R: Maximizing the pseudolikelihood. > test-offsets.R: Finished MPLE. > test-offsets.R: Evaluating log-likelihood at the estimate. > test-offsets.R: > test-networkLite.R: Starting contrastive divergence estimation via CD-MCMLE: > test-networkLite.R: Iteration 1 of at most 60: > test-networkLite.R: Convergence test P-value:1e-01 > test-networkLite.R: 1 > test-networkLite.R: The log-likelihood improved by 0.01081. > test-networkLite.R: Iteration 2 of at most 60: > test-networkLite.R: Convergence test P-value:8.1e-01 > test-networkLite.R: Convergence detected. Stopping. > test-networkLite.R: 1 > test-networkLite.R: The log-likelihood improved by 0.000224. > test-networkLite.R: Finished CD. > test-networkLite.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-networkLite.R: Iteration 1 of at most 60: > test-offsets.R: Starting maximum pseudolikelihood estimation (MPLE): > test-offsets.R: Obtaining the responsible dyads. > test-offsets.R: Evaluating the predictor and response matrix. > test-offsets.R: Maximizing the pseudolikelihood. > test-offsets.R: Finished MPLE. > test-offsets.R: Evaluating log-likelihood at the estimate. > test-offsets.R: > test-networkLite.R: 1 Optimizing with step length 1.0000. > test-networkLite.R: The log-likelihood improved by 1.0636. > test-networkLite.R: Estimating equations are not within tolerance region. > test-networkLite.R: Iteration 2 of at most 60: > test-offsets.R: Starting maximum pseudolikelihood estimation (MPLE): > test-offsets.R: Obtaining the responsible dyads. > test-offsets.R: Evaluating the predictor and response matrix. > test-offsets.R: Maximizing the pseudolikelihood. > test-offsets.R: Finished MPLE. > test-networkLite.R: 1 Optimizing with step length 1.0000. > test-offsets.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-offsets.R: Iteration 1 of at most 2: > test-networkLite.R: The log-likelihood improved by 0.0078. > test-networkLite.R: Convergence test p-value: 0.0012. Converged with 99% confidence. > test-networkLite.R: Finished MCMLE. > test-networkLite.R: This model was fit using MCMC. To examine model diagnostics and check > test-networkLite.R: for degeneracy, use the mcmc.diagnostics() function. > test-networkLite.R: Best valid proposal 'Unif' cannot take into account hint(s) 'sparse' and 'triadic'. > test-networkLite.R: Best valid proposal 'Unif' cannot take into account hint(s) 'sparse'. > test-networkLite.R: Starting contrastive divergence estimation via CD-MCMLE: > test-networkLite.R: Iteration 1 of at most 60: > test-networkLite.R: Convergence test P-value:9.2e-01 > test-networkLite.R: Convergence detected. Stopping. > test-networkLite.R: 1 > test-networkLite.R: The log-likelihood improved by < 0.0001. > test-networkLite.R: Finished CD. > test-networkLite.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-networkLite.R: Iteration 1 of at most 60: > test-networkLite.R: 1 > test-networkLite.R: Optimizing with step length 1.0000. > test-networkLite.R: The log-likelihood improved by 0.1926. > test-networkLite.R: Estimating equations are not within tolerance region. > test-networkLite.R: Iteration 2 of at most 60: > test-offsets.R: 1 Optimizing with step length 1.0000. > test-offsets.R: The log-likelihood improved by 0.6004. > test-offsets.R: Estimating equations are not within tolerance region. > test-offsets.R: Iteration 2 of at most 2: > test-networkLite.R: 1 > test-networkLite.R: Optimizing with step length 1.0000. > test-networkLite.R: The log-likelihood improved by 0.0160. > test-networkLite.R: Convergence test p-value: 0.0001. > test-networkLite.R: Converged with 99% confidence. > test-networkLite.R: Finished MCMLE. > test-networkLite.R: This model was fit using MCMC. To examine model diagnostics and check > test-networkLite.R: for degeneracy, use the mcmc.diagnostics() function. > test-networkLite.R: Best valid proposal 'Unif' cannot take into account hint(s) 'sparse'. > test-networkLite.R: Best valid proposal 'Unif' cannot take into account hint(s) 'sparse'. > test-networkLite.R: Starting contrastive divergence estimation via CD-MCMLE: > test-networkLite.R: Iteration 1 of at most 60: > test-networkLite.R: Convergence test P-value:9.2e-01 > test-networkLite.R: Convergence detected. Stopping. > test-networkLite.R: 1 The log-likelihood improved by < 0.0001. > test-networkLite.R: Finished CD. > test-networkLite.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-networkLite.R: Iteration 1 of at most 60: > test-offsets.R: 1 > test-offsets.R: Optimizing with step length 1.0000. > test-networkLite.R: 1 > test-networkLite.R: Optimizing with step length 1.0000. > test-offsets.R: The log-likelihood improved by 0.0061. > test-networkLite.R: The log-likelihood improved by 0.1926. > test-networkLite.R: Estimating equations are not within tolerance region. > test-networkLite.R: Iteration 2 of at most 60: > test-offsets.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-offsets.R: Finished MCMLE. > test-offsets.R: Evaluating log-likelihood at the estimate. > test-offsets.R: Fitting the dyad-independent submodel... > test-networkLite.R: 1 > test-networkLite.R: Optimizing with step length 1.0000. > test-networkLite.R: The log-likelihood improved by 0.0160. > test-networkLite.R: Convergence test p-value: 0.0001. Converged with 99% confidence. > test-networkLite.R: Finished MCMLE. > test-networkLite.R: This model was fit using MCMC. To examine model diagnostics and check > test-networkLite.R: for degeneracy, use the mcmc.diagnostics() function. > test-offsets.R: Bridging between the dyad-independent submodel and the full model... > test-offsets.R: Setting up bridge sampling... > test-networkLite.R: Best valid proposal 'Unif' cannot take into account hint(s) 'sparse'. > test-offsets.R: Using 16 bridges: 1 > test-offsets.R: 2 > test-offsets.R: 3 > test-offsets.R: 4 > test-offsets.R: 5 > test-offsets.R: 6 > test-offsets.R: 7 > test-offsets.R: 8 > test-offsets.R: 9 > test-offsets.R: 10 > test-networkLite.R: Best valid proposal 'Unif' cannot take into account hint(s) 'sparse' and 'triadic'. > test-offsets.R: 11 > test-offsets.R: 12 > test-networkLite.R: Starting contrastive divergence estimation via CD-MCMLE: > test-networkLite.R: Iteration 1 of at most 60: > test-networkLite.R: Convergence test P-value:3e-01 > test-offsets.R: 13 > test-networkLite.R: 1 > test-networkLite.R: The log-likelihood improved by 0.004165. > test-networkLite.R: Iteration 2 of at most 60: > test-networkLite.R: Convergence test P-value:1.2e-01 > test-offsets.R: 14 > test-networkLite.R: 1 > test-networkLite.R: The log-likelihood improved by 0.009777. > test-networkLite.R: Iteration 3 of at most 60: > test-networkLite.R: Convergence test P-value:6.5e-01 > test-networkLite.R: Convergence detected. Stopping. > test-offsets.R: 15 > test-networkLite.R: 1 > test-networkLite.R: The log-likelihood improved by 0.0008154. > test-networkLite.R: Finished CD. > test-networkLite.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-networkLite.R: Iteration 1 of at most 60: > test-offsets.R: 16 > test-offsets.R: . > test-offsets.R: Bridging finished. > test-offsets.R: > test-offsets.R: This model was fit using MCMC. To examine model diagnostics and check > test-offsets.R: for degeneracy, use the mcmc.diagnostics() function. > test-offsets.R: Starting maximum pseudolikelihood estimation (MPLE): > test-offsets.R: Obtaining the responsible dyads. > test-offsets.R: Evaluating the predictor and response matrix. > test-offsets.R: Maximizing the pseudolikelihood. > test-offsets.R: Finished MPLE. > test-offsets.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-offsets.R: Iteration 1 of at most 2: > test-networkLite.R: 1 > test-networkLite.R: Optimizing with step length 1.0000. > test-networkLite.R: The log-likelihood improved by 0.7841. > test-networkLite.R: Estimating equations are not within tolerance region. > test-networkLite.R: Iteration 2 of at most 60: > test-networkLite.R: 1 > test-networkLite.R: Optimizing with step length 1.0000. > test-networkLite.R: The log-likelihood improved by 0.0006. > test-networkLite.R: Convergence test p-value: 0.0458. Not converged with 99% confidence; increasing sample size. > test-networkLite.R: Iteration 3 of at most 60: > test-networkLite.R: 1 > test-networkLite.R: Optimizing with step length 1.0000. > test-networkLite.R: The log-likelihood improved by 0.0803. > test-networkLite.R: Convergence test p-value: 0.0031. > test-networkLite.R: Converged with 99% confidence. > test-networkLite.R: Finished MCMLE. > test-networkLite.R: This model was fit using MCMC. To examine model diagnostics and check > test-networkLite.R: for degeneracy, use the mcmc.diagnostics() function. > test-networkLite.R: Best valid proposal 'Unif' cannot take into account hint(s) 'sparse' and 'triadic'. > test-networkLite.R: Best valid proposal 'Unif' cannot take into account hint(s) 'sparse' and 'triadic'. > test-networkLite.R: Starting contrastive divergence estimation via CD-MCMLE: > test-networkLite.R: Iteration 1 of at most 60: > test-networkLite.R: Convergence test P-value:3e-01 > test-networkLite.R: 1 > test-networkLite.R: The log-likelihood improved by 0.004165. > test-networkLite.R: Iteration 2 of at most 60: > test-networkLite.R: Convergence test P-value:1.2e-01 > test-networkLite.R: 1 > test-networkLite.R: The log-likelihood improved by 0.009777. > test-networkLite.R: Iteration 3 of at most 60: > test-networkLite.R: Convergence test P-value:6.5e-01 > test-networkLite.R: Convergence detected. Stopping. > test-networkLite.R: 1 > test-networkLite.R: The log-likelihood improved by 0.0008154. > test-networkLite.R: Finished CD. > test-networkLite.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-networkLite.R: Iteration 1 of at most 60: > test-networkLite.R: 1 > test-networkLite.R: Optimizing with step length 1.0000. > test-networkLite.R: The log-likelihood improved by 0.7841. > test-networkLite.R: Estimating equations are not within tolerance region. > test-networkLite.R: Iteration 2 of at most 60: > test-networkLite.R: 1 Optimizing with step length 1.0000. > test-networkLite.R: The log-likelihood improved by 0.0006. > test-networkLite.R: Convergence test p-value: 0.0458. > test-networkLite.R: Not converged with 99% confidence; increasing sample size. > test-networkLite.R: Iteration 3 of at most 60: > test-networkLite.R: 1 > test-networkLite.R: Optimizing with step length 1.0000. > test-networkLite.R: The log-likelihood improved by 0.0803. > test-networkLite.R: Convergence test p-value: 0.0031. > test-networkLite.R: Converged with 99% confidence. > test-networkLite.R: Finished MCMLE. > test-networkLite.R: This model was fit using MCMC. To examine model diagnostics and check > test-networkLite.R: for degeneracy, use the mcmc.diagnostics() function. > test-networkLite.R: Best valid proposal 'Unif' cannot take into account hint(s) 'sparse' and 'triadic'. > test-operators.R: Starting maximum pseudolikelihood estimation (MPLE): > test-operators.R: Obtaining the responsible dyads. > test-operators.R: Evaluating the predictor and response matrix. > test-operators.R: Maximizing the pseudolikelihood. > test-operators.R: Finished MPLE. > test-operators.R: Starting maximum pseudolikelihood estimation (MPLE): > test-operators.R: Obtaining the responsible dyads. > test-operators.R: Evaluating the predictor and response matrix. > test-operators.R: Maximizing the pseudolikelihood. > test-operators.R: Finished MPLE. > test-operators.R: Starting maximum pseudolikelihood estimation (MPLE): > test-operators.R: Obtaining the responsible dyads. > test-operators.R: Evaluating the predictor and response matrix. > test-operators.R: Maximizing the pseudolikelihood. > test-operators.R: Finished MPLE. > test-operators.R: Starting maximum pseudolikelihood estimation (MPLE): > test-operators.R: Obtaining the responsible dyads. > test-operators.R: Evaluating the predictor and response matrix. > test-operators.R: Maximizing the pseudolikelihood. > test-operators.R: Finished MPLE. > test-operators.R: Starting maximum pseudolikelihood estimation (MPLE): > test-operators.R: Obtaining the responsible dyads. > test-operators.R: Evaluating the predictor and response matrix. > test-operators.R: Maximizing the pseudolikelihood. > test-operators.R: Finished MPLE. > test-operators.R: Starting maximum pseudolikelihood estimation (MPLE): > test-operators.R: Obtaining the responsible dyads. > test-operators.R: Evaluating the predictor and response matrix. > test-operators.R: Maximizing the pseudolikelihood. > test-operators.R: Finished MPLE. > test-operators.R: Starting maximum pseudolikelihood estimation (MPLE): > test-operators.R: Obtaining the responsible dyads. > test-operators.R: Evaluating the predictor and response matrix. > test-operators.R: Maximizing the pseudolikelihood. > test-operators.R: Finished MPLE. > test-operators.R: Starting maximum pseudolikelihood estimation (MPLE): > test-operators.R: Obtaining the responsible dyads. > test-operators.R: Evaluating the predictor and response matrix. > test-operators.R: Maximizing the pseudolikelihood. > test-operators.R: Finished MPLE. > test-operators.R: Starting maximum pseudolikelihood estimation (MPLE): > test-operators.R: Obtaining the responsible dyads. > test-operators.R: Evaluating the predictor and response matrix. > test-operators.R: Maximizing the pseudolikelihood. > test-operators.R: Finished MPLE. > test-parallel.R: > test-parallel.R: Attaching package: 'statnet.common' > test-parallel.R: > test-parallel.R: The following objects are masked from 'package:base': > test-parallel.R: > test-parallel.R: attr, order, replace > test-parallel.R: > test-parallel.R: parallel test(s) skipped. Set ENABLE_statnet_TESTS environment variable to run. > test-parallel.R: Skipping OpenMP test. This package installation was built without OpenMP support. > test-predict.ergm.R: Starting maximum pseudolikelihood estimation (MPLE): > test-predict.ergm.R: Obtaining the responsible dyads. > test-predict.ergm.R: Evaluating the predictor and response matrix. > test-predict.ergm.R: Maximizing the pseudolikelihood. > test-predict.ergm.R: Finished MPLE. > test-predict.ergm.R: Starting maximum pseudolikelihood estimation (MPLE): > test-predict.ergm.R: Obtaining the responsible dyads. > test-predict.ergm.R: Evaluating the predictor and response matrix. > test-predict.ergm.R: Maximizing the pseudolikelihood. > test-predict.ergm.R: Finished MPLE. > test-predict.ergm.R: Starting maximum pseudolikelihood estimation (MPLE): > test-predict.ergm.R: Obtaining the responsible dyads. > test-predict.ergm.R: Evaluating the predictor and response matrix. > test-predict.ergm.R: Maximizing the pseudolikelihood. > test-predict.ergm.R: Finished MPLE. > test-offsets.R: 1 > test-offsets.R: Optimizing with step length 1.0000. > test-offsets.R: The log-likelihood improved by 0.7959. > test-offsets.R: Estimating equations are not within tolerance region. > test-offsets.R: Iteration 2 of at most 2: > test-predict.ergm.R: Starting maximum pseudolikelihood estimation (MPLE): > test-predict.ergm.R: Obtaining the responsible dyads. > test-predict.ergm.R: Evaluating the predictor and response matrix. > test-predict.ergm.R: Maximizing the pseudolikelihood. > test-predict.ergm.R: Finished MPLE. > test-predict.ergm.R: Starting maximum pseudolikelihood estimation (MPLE): > test-predict.ergm.R: Obtaining the responsible dyads. > test-predict.ergm.R: Evaluating the predictor and response matrix. > test-predict.ergm.R: Maximizing the pseudolikelihood. > test-predict.ergm.R: Finished MPLE. > test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'. > test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'. > test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'. > test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'. > test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'. > test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'. > test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'. > test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'. > test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'. > test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'. > test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'. > test-offsets.R: 1 Optimizing with step length 1.0000. > test-offsets.R: The log-likelihood improved by 0.0207. > test-offsets.R: Convergence test p-value: 0.0005. Converged with 99% confidence. > test-offsets.R: Finished MCMLE. > test-offsets.R: Evaluating log-likelihood at the estimate. > test-offsets.R: Fitting the dyad-independent submodel... > test-offsets.R: Bridging between the dyad-independent submodel and the full model... > test-offsets.R: Setting up bridge sampling... > test-offsets.R: Using 16 bridges: > test-offsets.R: 1 > test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'. > test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'. > test-offsets.R: 2 > test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'. > test-offsets.R: 3 > test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'. > test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'. > test-offsets.R: 4 > test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'. > test-offsets.R: 5 > test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'. > test-offsets.R: 6 > test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'. > test-offsets.R: 7 > test-offsets.R: 8 > test-offsets.R: 9 > test-offsets.R: 10 > test-offsets.R: 11 > test-offsets.R: 12 > test-offsets.R: 13 > test-offsets.R: 14 > test-offsets.R: 15 > test-offsets.R: 16 > test-offsets.R: . > test-offsets.R: Bridging finished. > test-offsets.R: > test-offsets.R: This model was fit using MCMC. To examine model diagnostics and check > test-offsets.R: for degeneracy, use the mcmc.diagnostics() function. > test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'. > test-runtime-diags.R: Starting maximum pseudolikelihood estimation (MPLE): > test-runtime-diags.R: Obtaining the responsible dyads. > test-runtime-diags.R: Evaluating the predictor and response matrix. > test-runtime-diags.R: Maximizing the pseudolikelihood. > test-runtime-diags.R: Finished MPLE. > test-runtime-diags.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-runtime-diags.R: Iteration 1 of at most 60: > test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'. > test-runtime-diags.R: 1 > test-runtime-diags.R: Optimizing with step length 1.0000. > test-runtime-diags.R: The log-likelihood improved by 0.0414. > test-runtime-diags.R: Convergence test p-value: 0.0016. Converged with 99% confidence. > test-runtime-diags.R: Finished MCMLE. > test-runtime-diags.R: This model was fit using MCMC. To examine model diagnostics and check > test-runtime-diags.R: for degeneracy, use the mcmc.diagnostics() function. > test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'. > test-scoping.R: Starting maximum pseudolikelihood estimation (MPLE): > test-scoping.R: Obtaining the responsible dyads. > test-scoping.R: Evaluating the predictor and response matrix. > test-scoping.R: Maximizing the pseudolikelihood. > test-scoping.R: Finished MPLE. > test-scoping.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-scoping.R: Iteration 1 of at most 1: > test-scoping.R: 1 > test-scoping.R: Optimizing with step length 1.0000. > test-scoping.R: The log-likelihood improved by 0.2011. > test-scoping.R: Estimating equations are not within tolerance region. > test-scoping.R: MCMLE estimation did not converge after 1 iterations. The estimated coefficients may not be accurate. Estimation may be resumed by passing the coefficients as initial values; see 'init' under ?control.ergm for details. > test-scoping.R: Finished MCMLE. > test-scoping.R: Evaluating log-likelihood at the estimate. > test-scoping.R: Fitting the dyad-independent submodel... > test-scoping.R: Bridging between the dyad-independent submodel and the full model... > test-scoping.R: Setting up bridge sampling... > test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'. > test-scoping.R: Using 16 bridges: 1 > test-scoping.R: 2 > test-scoping.R: 3 > test-scoping.R: 4 > test-scoping.R: 5 > test-scoping.R: 6 > test-scoping.R: 7 > test-scoping.R: 8 > test-scoping.R: 9 > test-scoping.R: 10 > test-scoping.R: 11 > test-scoping.R: 12 > test-scoping.R: 13 > test-scoping.R: 14 > test-scoping.R: 15 > test-scoping.R: 16 > test-scoping.R: . > test-scoping.R: Bridging finished. > test-scoping.R: > test-scoping.R: This model was fit using MCMC. To examine model diagnostics and check > test-scoping.R: for degeneracy, use the mcmc.diagnostics() function. > test-scoping.R: Starting maximum pseudolikelihood estimation (MPLE): > test-scoping.R: Obtaining the responsible dyads. > test-scoping.R: Evaluating the predictor and response matrix. > test-scoping.R: Maximizing the pseudolikelihood. > test-scoping.R: Finished MPLE. > test-scoping.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-scoping.R: Iteration 1 of at most 1: > test-scoping.R: 1 > test-scoping.R: Optimizing with step length 1.0000. > test-scoping.R: The log-likelihood improved by 0.2011. > test-scoping.R: Estimating equations are not within tolerance region. > test-scoping.R: MCMLE estimation did not converge after 1 iterations. The estimated coefficients may not be accurate. Estimation may be resumed by passing the coefficients as initial values; see 'init' under ?control.ergm for details. > test-scoping.R: Finished MCMLE. > test-scoping.R: Evaluating log-likelihood at the estimate. > test-scoping.R: Fitting the dyad-independent submodel... > test-scoping.R: Bridging between the dyad-independent submodel and the full model... > test-scoping.R: Setting up bridge sampling... > test-scoping.R: Using 16 bridges: > test-scoping.R: 1 > test-scoping.R: 2 > test-scoping.R: 3 > test-scoping.R: 4 > test-scoping.R: 5 > test-scoping.R: 6 > test-scoping.R: 7 > test-scoping.R: 8 > test-scoping.R: 9 > test-scoping.R: 10 > test-scoping.R: 11 > test-scoping.R: 12 > test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'. > test-scoping.R: 13 > test-scoping.R: 14 > test-scoping.R: 15 > test-scoping.R: 16 > test-scoping.R: . > test-scoping.R: Bridging finished. > test-scoping.R: > test-scoping.R: This model was fit using MCMC. To examine model diagnostics and check > test-scoping.R: for degeneracy, use the mcmc.diagnostics() function. > test-shrink-into-CH.R: 1 > test-shrink-into-CH.R: 2 > test-shrink-into-CH.R: 3 > test-shrink-into-CH.R: 4 > test-shrink-into-CH.R: 5 > test-shrink-into-CH.R: 6 > test-shrink-into-CH.R: 7 > test-shrink-into-CH.R: 8 > test-shrink-into-CH.R: 9 > test-shrink-into-CH.R: 10 > test-shrink-into-CH.R: 11 > test-shrink-into-CH.R: 12 > test-shrink-into-CH.R: 13 > test-shrink-into-CH.R: 14 > test-shrink-into-CH.R: 15 > test-shrink-into-CH.R: 16 > test-shrink-into-CH.R: 17 > test-shrink-into-CH.R: 18 > test-shrink-into-CH.R: 19 > test-shrink-into-CH.R: 20 > test-shrink-into-CH.R: 1 > test-shrink-into-CH.R: 2 > test-shrink-into-CH.R: 3 > test-shrink-into-CH.R: 4 > test-shrink-into-CH.R: 5 > test-shrink-into-CH.R: 6 > test-shrink-into-CH.R: 7 > test-shrink-into-CH.R: 8 > test-shrink-into-CH.R: 9 > test-shrink-into-CH.R: 10 > test-shrink-into-CH.R: 11 > test-shrink-into-CH.R: 12 > test-shrink-into-CH.R: 13 > test-shrink-into-CH.R: 14 > test-shrink-into-CH.R: 15 > test-shrink-into-CH.R: 16 > test-shrink-into-CH.R: 17 > test-shrink-into-CH.R: 18 > test-shrink-into-CH.R: 19 > test-shrink-into-CH.R: 20 > test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'. > test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'. > test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'. > test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'. > test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'. > test-skip.R: Starting maximum pseudolikelihood estimation (MPLE): > test-skip.R: Obtaining the responsible dyads. > test-skip.R: Evaluating the predictor and response matrix. > test-skip.R: Maximizing the pseudolikelihood. > test-skip.R: Finished MPLE. > test-skip.R: Evaluating log-likelihood at the estimate. > test-skip.R: > test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'. > test-skip.R: Starting maximum pseudolikelihood estimation (MPLE): > test-skip.R: Obtaining the responsible dyads. > test-skip.R: Evaluating the predictor and response matrix. > test-skip.R: Maximizing the pseudolikelihood. > test-skip.R: Finished MPLE. > test-skip.R: Evaluating log-likelihood at the estimate. > test-skip.R: > test-skip.R: Starting maximum pseudolikelihood estimation (MPLE): > test-skip.R: Obtaining the responsible dyads. > test-skip.R: Evaluating the predictor and response matrix. > test-skip.R: Maximizing the pseudolikelihood. > test-skip.R: Finished MPLE. > test-skip.R: Evaluating log-likelihood at the estimate. > test-skip.R: > test-skip.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-skip.R: Iteration 1 of at most 60: > test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'. > test-skip.R: 1 > test-skip.R: Optimizing with step length 1.0000. > test-skip.R: The log-likelihood improved by 0.0360. > test-skip.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-skip.R: Finished MCMLE. > test-skip.R: This model was fit using MCMC. To examine model diagnostics and check > test-skip.R: for degeneracy, use the mcmc.diagnostics() function. > test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'. > test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'. > test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'. > test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'. > test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'. > test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'. > test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'. > test-snctrl.R: Starting maximum pseudolikelihood estimation (MPLE): > test-snctrl.R: Obtaining the responsible dyads. > test-snctrl.R: Evaluating the predictor and response matrix. > test-snctrl.R: Maximizing the pseudolikelihood. > test-snctrl.R: Finished MPLE. > test-snctrl.R: Evaluating log-likelihood at the estimate. > test-snctrl.R: > test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'. > test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'. > test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'. > test-stocapprox.R: Starting maximum pseudolikelihood estimation (MPLE): > test-stocapprox.R: Obtaining the responsible dyads. > test-stocapprox.R: Evaluating the predictor and response matrix. > test-stocapprox.R: Maximizing the pseudolikelihood. > test-stocapprox.R: Finished MPLE. > test-stocapprox.R: edges triangle > test-stocapprox.R: -1.7009355 0.2208488 > test-stocapprox.R: Stochastic approximation algorithm with theta_0 equal to: > test-stocapprox.R: Starting burnin of 16384 steps > test-stocapprox.R: Phase 1: 200 steps (interval = 1024) > test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'. > test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'. > test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'. > test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'. > test-stocapprox.R: Stochastic Approximation estimate: > test-stocapprox.R: edges triangle > test-stocapprox.R: -1.6617183 0.1405334 > test-stocapprox.R: Phase 3: 1000 iterations > test-stocapprox.R: (interval=1024) > test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'. > test-stocapprox.R: This model was fit using MCMC. To examine model diagnostics and check > test-stocapprox.R: for degeneracy, use the mcmc.diagnostics() function. > test-stocapprox.R: Starting maximum pseudolikelihood estimation (MPLE): > test-stocapprox.R: Obtaining the responsible dyads. > test-stocapprox.R: Evaluating the predictor and response matrix. > test-stocapprox.R: Maximizing the pseudolikelihood. > test-stocapprox.R: Finished MPLE. > test-stocapprox.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-stocapprox.R: Iteration 1 of at most 60: > test-stocapprox.R: 1 > test-stocapprox.R: Optimizing with step length 1.0000. > test-stocapprox.R: The log-likelihood improved by 0.0034. > test-stocapprox.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-stocapprox.R: Finished MCMLE. > test-stocapprox.R: This model was fit using MCMC. To examine model diagnostics and check > test-stocapprox.R: for degeneracy, use the mcmc.diagnostics() function. > test-stocapprox.R: Starting maximum pseudolikelihood estimation (MPLE): > test-stocapprox.R: Obtaining the responsible dyads. > test-stocapprox.R: Evaluating the predictor and response matrix. > test-stocapprox.R: Maximizing the pseudolikelihood. > test-stocapprox.R: Finished MPLE. > test-stocapprox.R: edges gwdegree gwdegree.decay > test-stocapprox.R: -1.5333754 -0.1317716 0.6729982 > test-stocapprox.R: Stochastic approximation algorithm with theta_0 equal to: > test-stocapprox.R: Starting burnin of 16384 steps > test-stocapprox.R: Phase 1: 200 steps (interval = 1024) > test-stocapprox.R: Stochastic Approximation estimate: > test-stocapprox.R: edges gwdegree gwdegree.decay > test-stocapprox.R: -1.57231795 -0.05712682 0.44962020 > test-stocapprox.R: Phase 3: 1000 iterations > test-stocapprox.R: (interval=1024) > test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'. > test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'. > test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'. > test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'. > test-stocapprox.R: This model was fit using MCMC. To examine model diagnostics and check > test-stocapprox.R: for degeneracy, use the mcmc.diagnostics() function. > test-stocapprox.R: Starting maximum pseudolikelihood estimation (MPLE): > test-stocapprox.R: Obtaining the responsible dyads. > test-stocapprox.R: Evaluating the predictor and response matrix. > test-stocapprox.R: Maximizing the pseudolikelihood. > test-stocapprox.R: Finished MPLE. > test-stocapprox.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-stocapprox.R: Iteration 1 of at most 60: > test-stocapprox.R: 1 > test-stocapprox.R: Optimizing with step length 1.0000. > test-stocapprox.R: The log-likelihood improved by 0.0007. > test-stocapprox.R: Convergence test p-value: 0.0001. > test-stocapprox.R: Converged with 99% confidence. > test-stocapprox.R: Finished MCMLE. > test-stocapprox.R: This model was fit using MCMC. To examine model diagnostics and check > test-stocapprox.R: for degeneracy, use the mcmc.diagnostics() function. > test-stocapprox.R: Best valid proposal 'DiscUnif' cannot take into account hint(s) 'sparse' and 'triadic'. > test-stocapprox.R: Starting contrastive divergence estimation via CD-MCMLE: > test-stocapprox.R: Iteration 1 of at most 60: > test-stocapprox.R: Convergence test P-value:1.1e-111 > test-stocapprox.R: 1 > test-stocapprox.R: The log-likelihood improved by 1.945. > test-stocapprox.R: Iteration 2 of at most 60: > test-stocapprox.R: Convergence test P-value:1.4e-44 > test-stocapprox.R: 1 > test-stocapprox.R: The log-likelihood improved by 0.5962. > test-stocapprox.R: Iteration 3 of at most 60: > test-stocapprox.R: Convergence test P-value:4e-07 > test-stocapprox.R: 1 > test-stocapprox.R: The log-likelihood improved by 0.06364. > test-stocapprox.R: Iteration 4 of at most 60: > test-stocapprox.R: Convergence test P-value:5.6e-05 > test-stocapprox.R: 1 > test-stocapprox.R: The log-likelihood improved by 0.04097. > test-stocapprox.R: Iteration 5 of at most 60: > test-stocapprox.R: Convergence test P-value:9.3e-03 > test-stocapprox.R: 1 > test-stocapprox.R: The log-likelihood improved by 0.01842. > test-stocapprox.R: Iteration 6 of at most 60: > test-stocapprox.R: Convergence test P-value:5.9e-01 > test-stocapprox.R: Convergence detected. Stopping. > test-stocapprox.R: 1 > test-stocapprox.R: The log-likelihood improved by 0.002083. > test-stocapprox.R: Finished CD. > test-stocapprox.R: nonzero transitiveweights.min.max.min > test-stocapprox.R: -1.743217 0.112619 > test-stocapprox.R: Stochastic approximation algorithm with theta_0 equal to: > test-stocapprox.R: Starting burnin of 16384 steps > test-stocapprox.R: Phase 1: 200 steps (interval = 1024) > test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'. > test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'. > test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'. > test-stocapprox.R: Stochastic Approximation estimate: > test-stocapprox.R: nonzero transitiveweights.min.max.min > test-stocapprox.R: -1.7631980 0.1383531 > test-stocapprox.R: Phase 3: 1000 iterations > test-stocapprox.R: (interval=1024) > test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'. > test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'. > test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'. > test-stocapprox.R: This model was fit using MCMC. To examine model diagnostics and check > test-stocapprox.R: for degeneracy, use the mcmc.diagnostics() function. > test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'. > test-stocapprox.R: Best valid proposal 'DiscUnif' cannot take into account hint(s) 'sparse' and 'triadic'. > test-stocapprox.R: Starting contrastive divergence estimation via CD-MCMLE: > test-stocapprox.R: Iteration 1 of at most 60: > test-stocapprox.R: Convergence test P-value:1.4e-98 > test-stocapprox.R: 1 > test-stocapprox.R: The log-likelihood improved by 1.862. > test-stocapprox.R: Iteration 2 of at most 60: > test-stocapprox.R: Convergence test P-value:3.5e-30 > test-stocapprox.R: 1 > test-stocapprox.R: The log-likelihood improved by 0.3427. > test-stocapprox.R: Iteration 3 of at most 60: > test-stocapprox.R: Convergence test P-value:3e-09 > test-stocapprox.R: 1 > test-stocapprox.R: The log-likelihood improved by 0.08204. > test-stocapprox.R: Iteration 4 of at most 60: > test-stocapprox.R: Convergence test P-value:3.9e-02 > test-stocapprox.R: 1 > test-stocapprox.R: The log-likelihood improved by 0.01313. > test-stocapprox.R: Iteration 5 of at most 60: > test-stocapprox.R: Convergence test P-value:9.2e-02 > test-stocapprox.R: 1 > test-stocapprox.R: The log-likelihood improved by 0.009411. > test-stocapprox.R: Iteration 6 of at most 60: > test-stocapprox.R: Convergence test P-value:2.6e-01 > test-stocapprox.R: 1 The log-likelihood improved by 0.005336. > test-stocapprox.R: Iteration 7 of at most 60: > test-stocapprox.R: Convergence test P-value:7.9e-01 > test-stocapprox.R: Convergence detected. Stopping. > test-stocapprox.R: 1 > test-stocapprox.R: The log-likelihood improved by 0.0009177. > test-stocapprox.R: Finished CD. > test-stocapprox.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-stocapprox.R: Iteration 1 of at most 60: > test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'. > test-stocapprox.R: 1 Optimizing with step length 1.0000. > test-stocapprox.R: The log-likelihood improved by 0.0022. > test-stocapprox.R: Convergence test p-value: < 0.0001. > test-stocapprox.R: Converged with 99% confidence. > test-stocapprox.R: Finished MCMLE. > test-stocapprox.R: This model was fit using MCMC. To examine model diagnostics and check > test-stocapprox.R: for degeneracy, use the mcmc.diagnostics() function. > test-stocapprox.R: > test-stocapprox.R: 'ergm.count' 4.1.3 (2025-09-10), part of the Statnet Project > test-stocapprox.R: * 'news(package="ergm.count")' for changes since last version > test-stocapprox.R: * 'c > test-stocapprox.R: itation("ergm.count")' for citation information > test-stocapprox.R: * 'https://statnet.org' for help, support, and other information > test-stocapprox.R: > test-target-offset.R: Unable to match target stats. Using MCMLE estimation. > test-target-offset.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-target-offset.R: Iteration 1 of at most 60: > test-target-offset.R: 1 > test-target-offset.R: Optimizing with step length 1.0000. > test-target-offset.R: The log-likelihood improved by 0.0210. > test-target-offset.R: Convergence test p-value: 0.0002. Converged with 99% confidence. > test-target-offset.R: Finished MCMLE. > test-target-offset.R: Evaluating log-likelihood at the estimate. > test-target-offset.R: Fitting the dyad-independent submodel... > test-target-offset.R: Bridging between the dyad-independent submodel and the full model... > test-target-offset.R: Setting up bridge sampling... > test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'. > test-target-offset.R: Using 16 bridges: 1 > test-target-offset.R: 2 > test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'. > test-target-offset.R: 3 > test-target-offset.R: 4 > test-target-offset.R: 5 > test-target-offset.R: 6 > test-target-offset.R: 7 > test-target-offset.R: 8 > test-target-offset.R: 9 > test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'. > test-target-offset.R: 10 > test-target-offset.R: 11 > test-target-offset.R: 12 > test-target-offset.R: 13 > test-target-offset.R: 14 > test-target-offset.R: 15 > test-target-offset.R: 16 > test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'. > test-target-offset.R: . > test-target-offset.R: Bridging finished. > test-target-offset.R: > test-target-offset.R: This model was fit using MCMC. To examine model diagnostics and check > test-target-offset.R: for degeneracy, use the mcmc.diagnostics() function. > test-target-offset.R: Sample statistics summary: > test-target-offset.R: > test-target-offset.R: Iterations = 14336:262144 > test-target-offset.R: Thinning interval = 1024 > test-target-offset.R: Number of chains = 1 > test-target-offset.R: Sample size per chain = 243 > test-target-offset.R: > test-target-offset.R: 1. Empirical mean and standard deviation for each variable, > test-target-offset.R: plus standard error of the mean: > test-target-offset.R: > test-target-offset.R: Mean SD Naive SE Time-series SE > test-target-offset.R: edges 0.55556 4.490 0.2880 0.2880 > test-target-offset.R: degree1 0.04527 2.005 0.1286 0.1286 > test-target-offset.R: > test-target-offset.R: 2. Quantiles for each variable: > test-target-offset.R: > test-target-offset.R: 2.5% 25% 50% 75% 97.5% > test-target-offset.R: edges -7 -2.5 0 3 9.00 > test-target-offset.R: degree1 -3 -1.0 0 1 4.95 > test-target-offset.R: > test-target-offset.R: > test-target-offset.R: Are sample statistics significantly different from observed? > test-target-offset.R: edges degree1 (Omni) > test-target-offset.R: diff. 0.55555556 0.04526749 NA > test-target-offset.R: test stat. 1.92893422 0.35200754 9.455405747 > test-target-offset.R: P-val. 0.05373903 0.72483261 0.009922641 > test-target-offset.R: > test-target-offset.R: Sample statistics cross-correlations: > test-target-offset.R: edges degree1 > test-target-offset.R: edges 1.0000000 -0.7250142 > test-target-offset.R: degree1 -0.7250142 1.0000000 > test-target-offset.R: > test-target-offset.R: Sample statistics auto-correlation: > test-target-offset.R: Chain 1 > test-target-offset.R: edges degree1 > test-target-offset.R: Lag 0 1.000000000 1.000000000 > test-target-offset.R: Lag 1024 -0.061427219 0.030194492 > test-target-offset.R: Lag 2048 0.007868535 0.092075103 > test-target-offset.R: Lag 3072 0.018624816 0.047810603 > test-target-offset.R: Lag 4096 -0.047471894 0.007659206 > test-target-offset.R: Lag 5120 -0.073593205 -0.009870131 > test-target-offset.R: > test-target-offset.R: Sample statistics burn-in diagnostic (Geweke): > test-target-offset.R: Chain 1 > test-target-offset.R: > test-target-offset.R: Fraction in 1st window = 0.1 > test-target-offset.R: Fraction in 2nd window = 0.5 > test-target-offset.R: > test-target-offset.R: edges degree1 > test-target-offset.R: 0.4498910 -0.1601093 > test-target-offset.R: > test-target-offset.R: Individual P-values (lower = worse): > test-target-offset.R: edges degree1 > test-target-offset.R: 0.652789 0.872795 > test-target-offset.R: Joint P-value (lower = worse): 0.8380775 > test-target-offset.R: > test-target-offset.R: Note: MCMC diagnostics shown here are from the last round of > test-target-offset.R: simulation, prior to computation of final parameter estimates. > test-target-offset.R: Because the final estimates are refinements of those used for this > test-target-offset.R: simulation run, these diagnostics may understate model performance. > test-target-offset.R: To directly assess the performance of the final model on in-model > test-target-offset.R: statistics, please use the GOF command: gof(ergmFitObject, > test-target-offset.R: GOF=~model). > test-target-offset.R: > test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'. > test-target-offset.R: Unable to match target stats. Using MCMLE estimation. > test-target-offset.R: Starting maximum pseudolikelihood estimation (MPLE): > test-target-offset.R: Obtaining the responsible dyads. > test-target-offset.R: Evaluating the predictor and response matrix. > test-target-offset.R: Maximizing the pseudolikelihood. > test-target-offset.R: Finished MPLE. > test-target-offset.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-target-offset.R: Iteration 1 of at most 3: > test-target-offset.R: 1 > test-target-offset.R: Optimizing with step length 0.7297. > test-target-offset.R: The log-likelihood improved by < 0.0001. > test-target-offset.R: Iteration 2 of at most 3: > test-proposal-bdstrattnt.R: Best valid proposal 'BDStratTNT' cannot take into account hint(s) 'triadic'. > test-target-offset.R: 1 > test-target-offset.R: Optimizing with step length 0.6389. > test-target-offset.R: The log-likelihood improved by < 0.0001. > test-target-offset.R: Iteration 3 of at most 3: > test-target-offset.R: 1 > test-target-offset.R: Optimizing with step length 0.8386. > test-target-offset.R: The log-likelihood improved by < 0.0001. > test-target-offset.R: MCMLE estimation did not converge after 3 iterations. The estimated coefficients may not be accurate. Estimation may be resumed by passing the coefficients as initial values; see 'init' under ?control.ergm for details. > test-target-offset.R: Finished MCMLE. > test-target-offset.R: Evaluating log-likelihood at the estimate. > test-target-offset.R: Fitting the dyad-independent submodel... > test-target-offset.R: Bridging between the dyad-independent submodel and the full model... > test-target-offset.R: Setting up bridge sampling... > test-target-offset.R: Using 16 bridges: 1 > test-target-offset.R: 2 > test-target-offset.R: 3 > test-target-offset.R: 4 > test-target-offset.R: 5 > test-target-offset.R: 6 > test-target-offset.R: 7 > test-target-offset.R: 8 > test-target-offset.R: 9 > test-target-offset.R: 10 > test-target-offset.R: 11 > test-target-offset.R: 12 > test-target-offset.R: 13 > test-target-offset.R: 14 > test-target-offset.R: 15 > test-target-offset.R: 16 > test-target-offset.R: . > test-target-offset.R: Bridging finished. > test-target-offset.R: > test-target-offset.R: This model was fit using MCMC. To examine model diagnostics and check > test-target-offset.R: for degeneracy, use the mcmc.diagnostics() function. > test-target-offset.R: Sample statistics summary: > test-target-offset.R: > test-target-offset.R: Iterations = 3584:65536 > test-target-offset.R: Thinning interval = 256 > test-target-offset.R: Number of chains = 1 > test-target-offset.R: Sample size per chain = 243 > test-target-offset.R: > test-target-offset.R: 1. Empirical mean and standard deviation for each variable, > test-target-offset.R: plus standard error of the mean: > test-target-offset.R: > test-target-offset.R: Mean SD Naive SE Time-series SE > test-target-offset.R: edges 12.77778 5.118432 0.32835 0.3283476 > test-target-offset.R: gwdegree > test-target-offset.R: 0.84362 0.386003 0.02476 0.0247621 > test-target-offset.R: gwdegree.decay 0.01236 0.002961 0.00019 0.0001293 > test-target-offset.R: degree0 -0.84362 0.386003 0.02476 0.0247621 > test-target-offset.R: > test-target-offset.R: 2. Quantiles for each variable: > test-target-offset.R: > test-target-offset.R: 2.5% 25% 50% 75% 97.5% > test-target-offset.R: edges 3.000e+00 9.00000 13.00000 16.00000 23.00000 > test-target-offset.R: gwdegree 1.063e-12 1.00000 1.00000 1.00000 1.00000 > test-target-offset.R: gwdegree.decay 5.955e-03 0.01191 0.01191 0.01489 0.01489 > test-target-offset.R: degree0 -1.000e+00 -1.00000 -1.00000 -1.00000 0.00000 > test-target-offset.R: > test-target-offset.R: > test-target-offset.R: Sample statistics cross-correlations: > test-target-offset.R: edges gwdegree gwdegree.decay degree0 > test-target-offset.R: edges 1.0000000 0.2709648 0.6049364 -0.2709648 > test-target-offset.R: gwdegree 0.2709648 1.0000000 0.5143643 -1.0000000 > test-target-offset.R: gwdegree.decay 0.6049364 0.5143643 1.0000000 -0.5143643 > test-target-offset.R: degree0 -0.2709648 -1.0000000 -0.5143643 1.0000000 > test-target-offset.R: > test-target-offset.R: Sample statistics auto-correlation: > test-target-offset.R: Chain 1 > test-target-offset.R: edges gwdegree gwdegree.decay degree0 > test-target-offset.R: Lag 0 1.000000000 1.00000000 1.000000000 1.00000000 > test-target-offset.R: Lag 256 0.006056003 0.02865300 -0.034893817 0.02865300 > test-target-offset.R: Lag 512 0.062813023 -0.07862186 0.002608612 -0.07862186 > test-target-offset.R: Lag 768 -0.089332866 -0.07930006 -0.150790861 -0.07930006 > test-target-offset.R: Lag 1024 -0.091496281 -0.07564135 -0.189123113 -0.07564135 > test-target-offset.R: Lag 1280 0.030192390 0.03461402 0.073975025 0.03461402 > test-target-offset.R: > test-target-offset.R: Sample statistics burn-in diagnostic (Geweke): > test-target-offset.R: Chain 1 > test-target-offset.R: > test-target-offset.R: Fraction in 1st window = 0.1 > test-target-offset.R: Fraction in 2nd window = 0.5 > test-target-offset.R: > test-target-offset.R: edges gwdegree gwdegree.decay degree0 > test-target-offset.R: 0.05663036 -1.34419649 0.38772965 1.34419649 > test-target-offset.R: > test-target-offset.R: Individual P-values (lower = worse): > test-target-offset.R: edges gwdegree gwdegree.decay degree0 > test-target-offset.R: 0.9548397 0.1788849 0.6982161 0.1788849 > test-target-offset.R: Joint P-value (lower = worse): 0.3850888 > test-target-offset.R: > test-target-offset.R: Note: MCMC diagnostics shown here are from the last round of > test-target-offset.R: simulation, prior to computation of final parameter estimates. > test-target-offset.R: Because the final estimates are refinements of those used for this > test-target-offset.R: simulation run, these diagnostics may understate model performance. > test-target-offset.R: To directly assess the performance of the final model on in-model > test-target-offset.R: statistics, please use the GOF command: gof(ergmFitObject, > test-target-offset.R: GOF=~model). > test-target-offset.R: > test-term-Offset.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-Offset.R: Obtaining the responsible dyads. > test-term-Offset.R: Evaluating the predictor and response matrix. > test-term-Offset.R: Maximizing the pseudolikelihood. > test-term-Offset.R: Finished MPLE. > test-term-Offset.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-term-Offset.R: Iteration 1 of at most 60: > test-term-Offset.R: 1 > test-term-Offset.R: Optimizing with step length 1.0000. > test-term-Offset.R: The log-likelihood improved by 0.0066. > test-term-Offset.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-term-Offset.R: Finished MCMLE. > test-term-Offset.R: This model was fit using MCMC. To examine model diagnostics and check > test-term-Offset.R: for degeneracy, use the mcmc.diagnostics() function. > test-term-Offset.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-Offset.R: Obtaining the responsible dyads. > test-term-Offset.R: Evaluating the predictor and response matrix. > test-term-Offset.R: Maximizing the pseudolikelihood. > test-term-Offset.R: Finished MPLE. > test-term-Offset.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-term-Offset.R: Iteration 1 of at most 60: > test-term-Offset.R: 1 > test-term-Offset.R: Optimizing with step length 1.0000. > test-term-Offset.R: The log-likelihood improved by < 0.0001. > test-term-Offset.R: Convergence test p-value: 1.0000. Not converged with 99% confidence; increasing sample size. > test-term-Offset.R: Iteration 2 of at most 60: > test-term-Offset.R: 1 > test-term-Offset.R: Optimizing with step length 1.0000. > test-term-Offset.R: The log-likelihood improved by 0.0003. > test-term-Offset.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-term-Offset.R: Finished MCMLE. > test-term-Offset.R: This model was fit using MCMC. To examine model diagnostics and check > test-term-Offset.R: for degeneracy, use the mcmc.diagnostics() function. Saving _problems/test-term-Offset-21.R Saving _problems/test-term-Offset-25.R > test-term-b12nodematch.R: In term 'b1nodematch' in package 'ergm': Argument 'keep' has been superseded by 'levels', and it is recommended to use the latter. Note that its interpretation may be different. > test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-bipartite.R: Obtaining the responsible dyads. > test-term-bipartite.R: Evaluating the predictor and response matrix. > test-term-bipartite.R: Maximizing the pseudolikelihood. > test-term-bipartite.R: Finished MPLE. > test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-bipartite.R: Obtaining the responsible dyads. > test-term-bipartite.R: Evaluating the predictor and response matrix. > test-term-bipartite.R: Maximizing the pseudolikelihood. > test-term-bipartite.R: Finished MPLE. > test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-bipartite.R: Obtaining the responsible dyads. > test-term-bipartite.R: Evaluating the predictor and response matrix. > test-term-bipartite.R: Maximizing the pseudolikelihood. > test-term-bipartite.R: Finished MPLE. > test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-bipartite.R: Obtaining the responsible dyads. > test-term-bipartite.R: Evaluating the predictor and response matrix. > test-term-bipartite.R: Maximizing the pseudolikelihood. > test-term-bipartite.R: Finished MPLE. > test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-bipartite.R: Obtaining the responsible dyads. > test-term-bipartite.R: Evaluating the predictor and response matrix. > test-term-bipartite.R: Maximizing the pseudolikelihood. > test-term-bipartite.R: Finished MPLE. > test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-bipartite.R: Obtaining the responsible dyads. > test-term-bipartite.R: Evaluating the predictor and response matrix. > test-term-bipartite.R: Maximizing the pseudolikelihood. > test-term-bipartite.R: Finished MPLE. > test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-directed.R: Obtaining the responsible dyads. > test-term-directed.R: Evaluating the predictor and response matrix. > test-term-directed.R: Maximizing the pseudolikelihood. > test-term-directed.R: Finished MPLE. > test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-bipartite.R: Obtaining the responsible dyads. > test-term-bipartite.R: Evaluating the predictor and response matrix. > test-term-bipartite.R: Maximizing the pseudolikelihood. > test-term-bipartite.R: Finished MPLE. > test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-directed.R: Obtaining the responsible dyads. > test-term-directed.R: Evaluating the predictor and response matrix. > test-term-directed.R: Maximizing the pseudolikelihood. > test-term-directed.R: Finished MPLE. > test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-bipartite.R: Obtaining the responsible dyads. > test-term-bipartite.R: Evaluating the predictor and response matrix. > test-term-bipartite.R: Maximizing the pseudolikelihood. > test-term-bipartite.R: Finished MPLE. > test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-bipartite.R: Obtaining the responsible dyads. > test-term-bipartite.R: Evaluating the predictor and response matrix. > test-term-bipartite.R: Maximizing the pseudolikelihood. > test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-directed.R: Obtaining the responsible dyads. > test-term-directed.R: Evaluating the predictor and response matrix. > test-term-bipartite.R: Finished MPLE. > test-term-directed.R: Maximizing the pseudolikelihood. > test-term-directed.R: Finished MPLE. > test-term-bipartite.R: Observed statistic(s) b1dsp3 are at their smallest attainable values. Their coefficients will be fixed at -Inf. > test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-bipartite.R: Obtaining the responsible dyads. > test-term-bipartite.R: Evaluating the predictor and response matrix. > test-term-bipartite.R: Maximizing the pseudolikelihood. > test-term-bipartite.R: Finished MPLE. > test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-directed.R: Obtaining the responsible dyads. > test-term-directed.R: Evaluating the predictor and response matrix. > test-term-directed.R: Maximizing the pseudolikelihood. > test-term-directed.R: Finished MPLE. > test-term-directed.R: In term 'asymmetric' in package 'ergm': Argument 'keep' has been superseded by 'levels', and it is recommended to use the latter. Note that its interpretation may be different. > test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-bipartite.R: Obtaining the responsible dyads. > test-term-bipartite.R: Evaluating the predictor and response matrix. > test-term-bipartite.R: Maximizing the pseudolikelihood. > test-term-bipartite.R: Finished MPLE. > test-term-bipartite.R: In term 'b1factor' in package 'ergm': Argument 'base' has been superseded by 'levels', and it is recommended to use the latter. > test-term-bipartite.R: Note that its interpretation may be different. > test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-directed.R: Obtaining the responsible dyads. > test-term-directed.R: Evaluating the predictor and response matrix. > test-term-directed.R: Maximizing the pseudolikelihood. > test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-bipartite.R: Obtaining the responsible dyads. > test-term-bipartite.R: Evaluating the predictor and response matrix. > test-term-bipartite.R: Maximizing the pseudolikelihood. > test-term-directed.R: Finished MPLE. > test-term-bipartite.R: Finished MPLE. > test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-bipartite.R: Obtaining the responsible dyads. > test-term-bipartite.R: Evaluating the predictor and response matrix. > test-term-bipartite.R: Maximizing the pseudolikelihood. > test-term-bipartite.R: Finished MPLE. > test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-directed.R: Obtaining the responsible dyads. > test-term-directed.R: Evaluating the predictor and response matrix. > test-term-directed.R: Maximizing the pseudolikelihood. > test-term-directed.R: Finished MPLE. > test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-bipartite.R: Obtaining the responsible dyads. > test-term-bipartite.R: Evaluating the predictor and response matrix. > test-term-bipartite.R: Maximizing the pseudolikelihood. > test-term-bipartite.R: Finished MPLE. > test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-bipartite.R: Obtaining the responsible dyads. > test-term-bipartite.R: Evaluating the predictor and response matrix. > test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-directed.R: Obtaining the responsible dyads. > test-term-directed.R: Evaluating the predictor and response matrix. > test-term-bipartite.R: Maximizing the pseudolikelihood. > test-term-directed.R: Maximizing the pseudolikelihood. > test-term-bipartite.R: Finished MPLE. > test-term-directed.R: Finished MPLE. > test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-bipartite.R: Obtaining the responsible dyads. > test-term-bipartite.R: Evaluating the predictor and response matrix. > test-term-bipartite.R: Maximizing the pseudolikelihood. > test-term-bipartite.R: Finished MPLE. > test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-directed.R: Obtaining the responsible dyads. > test-term-directed.R: Evaluating the predictor and response matrix. > test-term-directed.R: Maximizing the pseudolikelihood. > test-term-directed.R: Finished MPLE. > test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-bipartite.R: Obtaining the responsible dyads. > test-term-bipartite.R: Evaluating the predictor and response matrix. > test-term-bipartite.R: Maximizing the pseudolikelihood. > test-term-bipartite.R: Finished MPLE. > test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-bipartite.R: Obtaining the responsible dyads. > test-term-bipartite.R: Evaluating the predictor and response matrix. > test-term-bipartite.R: Maximizing the pseudolikelihood. > test-term-bipartite.R: Finished MPLE. > test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-directed.R: Obtaining the responsible dyads. > test-term-directed.R: Evaluating the predictor and response matrix. > test-term-directed.R: Maximizing the pseudolikelihood. > test-term-directed.R: Finished MPLE. > test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-bipartite.R: Obtaining the responsible dyads. > test-term-bipartite.R: Evaluating the predictor and response matrix. > test-term-bipartite.R: Maximizing the pseudolikelihood. > test-term-bipartite.R: Finished MPLE. > test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-directed.R: Obtaining the responsible dyads. > test-term-directed.R: Evaluating the predictor and response matrix. > test-term-directed.R: Maximizing the pseudolikelihood. > test-term-directed.R: Finished MPLE. > test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-bipartite.R: Obtaining the responsible dyads. > test-term-bipartite.R: Evaluating the predictor and response matrix. > test-term-bipartite.R: Maximizing the pseudolikelihood. > test-term-bipartite.R: Finished MPLE. > test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-directed.R: Obtaining the responsible dyads. > test-term-directed.R: Evaluating the predictor and response matrix. > test-term-directed.R: Maximizing the pseudolikelihood. > test-term-directed.R: Finished MPLE. > test-term-directed.R: Observed statistic(s) ideg7+.homophily.group and ideg8+.homophily.group are at their smallest attainable values. Their coefficie > test-term-directed.R: nts will be fixed at -Inf. > test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-directed.R: Obtaining the responsible dyads. > test-term-directed.R: Evaluating the predictor and response matrix. > test-term-directed.R: Maximizing the pseudolikelihood. > test-term-directed.R: Finished MPLE. > test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-bipartite.R: Obtaining the responsible dyads. > test-term-bipartite.R: Evaluating the predictor and response matrix. > test-term-bipartite.R: Maximizing the pseudolikelihood. > test-term-bipartite.R: Finished MPLE. > test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-directed.R: Obtaining the responsible dyads. > test-term-directed.R: Evaluating the predictor and response matrix. > test-term-directed.R: Maximizing the pseudolikelihood. > test-term-directed.R: Finished MPLE. > test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-bipartite.R: Obtaining the responsible dyads. > test-term-bipartite.R: Evaluating the predictor and response matrix. > test-term-bipartite.R: Maximizing the pseudolikelihood. > test-term-bipartite.R: Finished MPLE. > test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-directed.R: Obtaining the responsible dyads. > test-term-directed.R: Evaluating the predictor and response matrix. > test-term-directed.R: Maximizing the pseudolikelihood. > test-term-directed.R: Finished MPLE. > test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-directed.R: Obtaining the responsible dyads. > test-term-directed.R: Evaluating the predictor and response matrix. > test-term-directed.R: Maximizing the pseudolikelihood. > test-term-directed.R: Finished MPLE. > test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-bipartite.R: Obtaining the responsible dyads. > test-term-bipartite.R: Evaluating the predictor and response matrix. > test-term-bipartite.R: Maximizing the pseudolikelihood. > test-term-bipartite.R: Finished MPLE. > test-term-bipartite.R: In term 'b1twostar' in package 'ergm': Argument 'base' has been superseded by 'levels2', and it is recommended to use the latter. Note that its interpretation may be different. > test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-directed.R: Obtaining the responsible dyads. > test-term-directed.R: Evaluating the predictor and response matrix. > test-term-directed.R: Maximizing the pseudolikelihood. > test-term-directed.R: Finished MPLE. > test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-bipartite.R: Obtaining the responsible dyads. > test-term-bipartite.R: Evaluating the predictor and response matrix. > test-term-bipartite.R: Maximizing the pseudolikelihood. > test-term-bipartite.R: Finished MPLE. > test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-directed.R: Obtaining the responsible dyads. > test-term-directed.R: Evaluating the predictor and response matrix. > test-term-directed.R: Maximizing the pseudolikelihood. > test-term-directed.R: Finished MPLE. > test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-directed.R: Obtaining the responsible dyads. > test-term-directed.R: Evaluating the predictor and response matrix. > test-term-directed.R: Maximizing the pseudolikelihood. > test-term-directed.R: Finished MPLE. > test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-bipartite.R: Obtaining the responsible dyads. > test-term-bipartite.R: Evaluating the predictor and response matrix. > test-term-bipartite.R: Maximizing the pseudolikelihood. > test-term-bipartite.R: Finished MPLE. > test-term-directed.R: Observed statistic(s) gwodeg.fixed.0 are at their greatest attainable values. Their coefficients will be fixed at +Inf. > test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-directed.R: Obtaining the responsible dyads. > test-term-directed.R: Evaluating the predictor and response matrix. > test-term-directed.R: Maximizing the pseudolikelihood. > test-term-directed.R: Finished MPLE. > test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-bipartite.R: Obtaining the responsible dyads. > test-term-bipartite.R: Evaluating the predictor and response matrix. > test-term-bipartite.R: Maximizing the pseudolikelihood. > test-term-bipartite.R: Finished MPLE. > test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-directed.R: Obtaining the responsible dyads. > test-term-directed.R: Evaluating the predictor and response matrix. > test-term-directed.R: Maximizing the pseudolikelihood. > test-term-directed.R: Finished MPLE. > test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-directed.R: Obtaining the responsible dyads. > test-term-directed.R: Evaluating the predictor and response matrix. > test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-bipartite.R: Obtaining the responsible dyads. > test-term-bipartite.R: Evaluating the predictor and response matrix. > test-term-bipartite.R: Maximizing the pseudolikelihood. > test-term-directed.R: Maximizing the pseudolikelihood. > test-term-directed.R: Finished MPLE. > test-term-bipartite.R: Finished MPLE. > test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-directed.R: Obtaining the responsible dyads. > test-term-directed.R: Evaluating the predictor and response matrix. > test-term-directed.R: Maximizing the pseudolikelihood. > test-term-directed.R: Finished MPLE. > test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-bipartite.R: Obtaining the responsible dyads. > test-term-bipartite.R: Evaluating the predictor and response matrix. > test-term-bipartite.R: Maximizing the pseudolikelihood. > test-term-bipartite.R: Finished MPLE. > test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-directed.R: Obtaining the responsible dyads. > test-term-directed.R: Evaluating the predictor and response matrix. > test-term-directed.R: Maximizing the pseudolikelihood. > test-term-directed.R: Finished MPLE. > test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-bipartite.R: Obtaining the responsible dyads. > test-term-bipartite.R: Evaluating the predictor and response matrix. > test-term-bipartite.R: Maximizing the pseudolikelihood. > test-term-bipartite.R: Finished MPLE. > test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-directed.R: Obtaining the responsible dyads. > test-term-directed.R: Evaluating the predictor and response matrix. > test-term-directed.R: Maximizing the pseudolikelihood. > test-term-directed.R: Finished MPLE. > test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-bipartite.R: Obtaining the responsible dyads. > test-term-bipartite.R: Evaluating the predictor and response matrix. > test-term-bipartite.R: Maximizing the pseudolikelihood. > test-term-bipartite.R: Finished MPLE. > test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-directed.R: Obtaining the responsible dyads. > test-term-directed.R: Evaluating the predictor and response matrix. > test-term-directed.R: Maximizing the pseudolikelihood. > test-term-directed.R: Finished MPLE. > test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-bipartite.R: Obtaining the responsible dyads. > test-term-bipartite.R: Evaluating the predictor and response matrix. > test-term-bipartite.R: Maximizing the pseudolikelihood. > test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-directed.R: Obtaining the responsible dyads. > test-term-directed.R: Evaluating the predictor and response matrix. > test-term-bipartite.R: Finished MPLE. > test-term-directed.R: Maximizing the pseudolikelihood. > test-term-directed.R: Finished MPLE. > test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-directed.R: Obtaining the responsible dyads. > test-term-directed.R: Evaluating the predictor and response matrix. > test-term-directed.R: Maximizing the pseudolikelihood. > test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-bipartite.R: Obtaining the responsible dyads. > test-term-bipartite.R: Evaluating the predictor and response matrix. > test-term-bipartite.R: Maximizing the pseudolikelihood. > test-term-directed.R: Finished MPLE. > test-term-bipartite.R: Finished MPLE. > test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-directed.R: Obtaining the responsible dyads. > test-term-directed.R: Evaluating the predictor and response matrix. > test-term-directed.R: Maximizing the pseudolikelihood. > test-term-directed.R: Finished MPLE. > test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-bipartite.R: Obtaining the responsible dyads. > test-term-bipartite.R: Evaluating the predictor and response matrix. > test-term-bipartite.R: Maximizing the pseudolikelihood. > test-term-bipartite.R: Finished MPLE. > test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-directed.R: Obtaining the responsible dyads. > test-term-directed.R: Evaluating the predictor and response matrix. > test-term-directed.R: Maximizing the pseudolikelihood. > test-term-directed.R: Finished MPLE. > test-term-bipartite.R: Observed statistic(s) b2dsp3 are at their smallest attainable values. Their coefficients will be fixed at -Inf. > test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-bipartite.R: Obtaining the responsible dyads. > test-term-bipartite.R: Evaluating the predictor and response matrix. > test-term-bipartite.R: Maximizing the pseudolikelihood. > test-term-bipartite.R: Finished MPLE. > test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-directed.R: Obtaining the responsible dyads. > test-term-directed.R: Evaluating the predictor and response matrix. > test-term-directed.R: Maximizing the pseudolikelihood. > test-term-directed.R: Finished MPLE. > test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-directed.R: Obtaining the responsible dyads. > test-term-directed.R: Evaluating the predictor and response matrix. > test-term-directed.R: Maximizing the pseudolikelihood. > test-term-directed.R: Finished MPLE. > test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-bipartite.R: Obtaining the responsible dyads. > test-term-bipartite.R: Evaluating the predictor and response matrix. > test-term-bipartite.R: Maximizing the pseudolikelihood. > test-term-bipartite.R: Finished MPLE. > test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-directed.R: Obtaining the responsible dyads. > test-term-directed.R: Evaluating the predictor and response matrix. > test-term-directed.R: Maximizing the pseudolikelihood. > test-term-directed.R: Finished MPLE. > test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-directed.R: Obtaining the responsible dyads. > test-term-directed.R: Evaluating the predictor and response matrix. > test-term-directed.R: Maximizing the pseudolikelihood. > test-term-directed.R: Finished MPLE. > test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-bipartite.R: Obtaining the responsible dyads. > test-term-bipartite.R: Evaluating the predictor and response matrix. > test-term-bipartite.R: Maximizing the pseudolikelihood. > test-term-bipartite.R: Finished MPLE. > test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-bipartite.R: Obtaining the responsible dyads. > test-term-bipartite.R: Evaluating the predictor and response matrix. > test-term-bipartite.R: Maximizing the pseudolikelihood. > test-term-bipartite.R: Finished MPLE. > test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-directed.R: Obtaining the responsible dyads. > test-term-directed.R: Evaluating the predictor and response matrix. > test-term-directed.R: Maximizing the pseudolikelihood. > test-term-directed.R: Finished MPLE. > test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-bipartite.R: Obtaining the responsible dyads. > test-term-bipartite.R: Evaluating the predictor and response matrix. > test-term-bipartite.R: Maximizing the pseudolikelihood. > test-term-bipartite.R: Finished MPLE. > test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-directed.R: Obtaining the responsible dyads. > test-term-directed.R: Evaluating the predictor and response matrix. > test-term-directed.R: Maximizing the pseudolikelihood. > test-term-directed.R: Finished MPLE. > test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-bipartite.R: Obtaining the responsible dyads. > test-term-bipartite.R: Evaluating the predictor and response matrix. > test-term-bipartite.R: Maximizing the pseudolikelihood. > test-term-bipartite.R: Finished MPLE. > test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-bipartite.R: Obtaining the responsible dyads. > test-term-bipartite.R: Evaluating the predictor and response matrix. > test-term-bipartite.R: Maximizing the pseudolikelihood. > test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-directed.R: Obtaining the responsible dyads. > test-term-directed.R: Evaluating the predictor and response matrix. > test-term-bipartite.R: Finished MPLE. > test-term-directed.R: Maximizing the pseudolikelihood. > test-term-directed.R: Finished MPLE. > test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-bipartite.R: Obtaining the responsible dyads. > test-term-bipartite.R: Evaluating the predictor and response matrix. > test-term-bipartite.R: Maximizing the pseudolikelihood. > test-term-bipartite.R: Finished MPLE. > test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-directed.R: Obtaining the responsible dyads. > test-term-directed.R: Evaluating the predictor and response matrix. > test-term-directed.R: Maximizing the pseudolikelihood. > test-term-directed.R: Finished MPLE. > test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-bipartite.R: Obtaining the responsible dyads. > test-term-bipartite.R: Evaluating the predictor and response matrix. > test-term-bipartite.R: Maximizing the pseudolikelihood. > test-term-bipartite.R: Finished MPLE. > test-term-directed.R: In term 'nodeifactor' in package 'ergm': Argument 'base' has been superseded by 'levels', and it is recommended to use the latter. Note that its interpretation may be different. > test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-directed.R: Obtaining the responsible dyads. > test-term-directed.R: Evaluating the predictor and response matrix. > test-term-directed.R: Maximizing the pseudolikelihood. > test-term-directed.R: Finished MPLE. > test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-bipartite.R: Obtaining the responsible dyads. > test-term-bipartite.R: Evaluating the predictor and response matrix. > test-term-bipartite.R: Maximizing the pseudolikelihood. > test-term-bipartite.R: Finished MPLE. > test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-directed.R: Obtaining the responsible dyads. > test-term-directed.R: Evaluating the predictor and response matrix. > test-term-directed.R: Maximizing the pseudolikelihood. > test-term-directed.R: Finished MPLE. > test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-bipartite.R: Obtaining the responsible dyads. > test-term-bipartite.R: Evaluating the predictor and response matrix. > test-term-bipartite.R: Maximizing the pseudolikelihood. > test-term-bipartite.R: Finished MPLE. > test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-directed.R: Obtaining the responsible dyads. > test-term-directed.R: Evaluating the predictor and response matrix. > test-term-directed.R: Maximizing the pseudolikelihood. > test-term-directed.R: Finished MPLE. > test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-bipartite.R: Obtaining the responsible dyads. > test-term-bipartite.R: Evaluating the predictor and response matrix. > test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-directed.R: Obtaining the responsible dyads. > test-term-directed.R: Evaluating the predictor and response matrix. > test-term-directed.R: Maximizing the pseudolikelihood. > test-term-bipartite.R: Maximizing the pseudolikelihood. > test-term-directed.R: Finished MPLE. > test-term-bipartite.R: Finished MPLE. > test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-directed.R: Obtaining the responsible dyads. > test-term-directed.R: Evaluating the predictor and response matrix. > test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-directed.R: Maximizing the pseudolikelihood. > test-term-bipartite.R: Obtaining the responsible dyads. > test-term-bipartite.R: Evaluating the predictor and response matrix. > test-term-directed.R: Finished MPLE. > test-term-bipartite.R: Maximizing the pseudolikelihood. > test-term-bipartite.R: Finished MPLE. > test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-directed.R: Obtaining the responsible dyads. > test-term-directed.R: Evaluating the predictor and response matrix. > test-term-directed.R: Maximizing the pseudolikelihood. > test-term-directed.R: Finished MPLE. > test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-bipartite.R: Obtaining the responsible dyads. > test-term-bipartite.R: Evaluating the predictor and response matrix. > test-term-bipartite.R: Maximizing the pseudolikelihood. > test-term-bipartite.R: Finished MPLE. > test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-directed.R: Obtaining the responsible dyads. > test-term-directed.R: Evaluating the predictor and response matrix. > test-term-directed.R: Maximizing the pseudolikelihood. > test-term-directed.R: Finished MPLE. > test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-bipartite.R: Obtaining the responsible dyads. > test-term-bipartite.R: Evaluating the predictor and response matrix. > test-term-bipartite.R: Maximizing the pseudolikelihood. > test-term-bipartite.R: Finished MPLE. > test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-directed.R: Obtaining the responsible dyads. > test-term-directed.R: Evaluating the predictor and response matrix. > test-term-directed.R: Maximizing the pseudolikelihood. > test-term-directed.R: Finished MPLE. > test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-directed.R: Obtaining the responsible dyads. > test-term-directed.R: Evaluating the predictor and response matrix. > test-term-directed.R: Maximizing the pseudolikelihood. > test-term-directed.R: Finished MPLE. > test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-directed.R: Obtaining the responsible dyads. > test-term-directed.R: Evaluating the predictor and response matrix. > test-term-directed.R: Maximizing the pseudolikelihood. > test-term-directed.R: Finished MPLE. > test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-bipartite.R: Obtaining the responsible dyads. > test-term-bipartite.R: Evaluating the predictor and response matrix. > test-term-bipartite.R: Maximizing the pseudolikelihood. > test-term-bipartite.R: Finished MPLE. > test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-directed.R: Obtaining the responsible dyads. > test-term-directed.R: Evaluating the predictor and response matrix. > test-term-directed.R: Maximizing the pseudolikelihood. > test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-bipartite.R: Obtaining the responsible dyads. > test-term-bipartite.R: Evaluating the predictor and response matrix. > test-term-bipartite.R: Maximizing the pseudolikelihood. > test-term-directed.R: Finished MPLE. > test-term-bipartite.R: Finished MPLE. > test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-bipartite.R: Obtaining the responsible dyads. > test-term-bipartite.R: Evaluating the predictor and response matrix. > test-term-bipartite.R: Maximizing the pseudolikelihood. > test-term-bipartite.R: Finished MPLE. > test-term-directed.R: Observed statistic(s) odeg7+ and odeg8+ are at their smallest attainable values. Their coefficients will be fixed at -Inf. > test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-directed.R: Obtaining the responsible dyads. > test-term-directed.R: Evaluating the predictor and response matrix. > test-term-directed.R: Maximizing the pseudolikelihood. > test-term-directed.R: Finished MPLE. > test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-bipartite.R: Obtaining the responsible dyads. > test-term-bipartite.R: Evaluating the predictor and response matrix. > test-term-bipartite.R: Maximizing the pseudolikelihood. > test-term-bipartite.R: Finished MPLE. > test-term-directed.R: Observed statistic(s) odeg6+.homophily.group, odeg7+.homophily.group, and odeg8+.homophily.group are at their smallest attainable values. Their coefficients will be fixed at -Inf. > test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-directed.R: Obtaining the responsible dyads. > test-term-directed.R: Evaluating the predictor and response matrix. > test-term-directed.R: Maximizing the pseudolikelihood. > test-term-directed.R: Finished MPLE. > test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-directed.R: Obtaining the responsible dyads. > test-term-directed.R: Evaluating the predictor and response matrix. > test-term-directed.R: Maximizing the pseudolikelihood. > test-term-directed.R: Finished MPLE. > test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-bipartite.R: Obtaining the responsible dyads. > test-term-bipartite.R: Evaluating the predictor and response matrix. > test-term-bipartite.R: Maximizing the pseudolikelihood. > test-term-bipartite.R: Finished MPLE. > test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-directed.R: Obtaining the responsible dyads. > test-term-directed.R: Evaluating the predictor and response matrix. > test-term-directed.R: Maximizing the pseudolikelihood. > test-term-directed.R: Finished MPLE. > test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-bipartite.R: Obtaining the responsible dyads. > test-term-bipartite.R: Evaluating the predictor and response matrix. > test-term-bipartite.R: Maximizing the pseudolikelihood. > test-term-bipartite.R: Finished MPLE. > test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-bipartite.R: Obtaining the responsible dyads. > test-term-bipartite.R: Evaluating the predictor and response matrix. > test-term-bipartite.R: Maximizing the pseudolikelihood. > test-term-bipartite.R: Finished MPLE. > test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-directed.R: Obtaining the responsible dyads. > test-term-directed.R: Evaluating the predictor and response matrix. > test-term-directed.R: Maximizing the pseudolikelihood. > test-term-directed.R: Finished MPLE. > test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-bipartite.R: Obtaining the responsible dyads. > test-term-bipartite.R: Evaluating the predictor and response matrix. > test-term-bipartite.R: Maximizing the pseudolikelihood. > test-term-bipartite.R: Finished MPLE. > test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-bipartite.R: Obtaining the responsible dyads. > test-term-bipartite.R: Evaluating the predictor and response matrix. > test-term-bipartite.R: Maximizing the pseudolikelihood. > test-term-bipartite.R: Finished MPLE. > test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-directed.R: Obtaining the responsible dyads. > test-term-directed.R: Evaluating the predictor and response matrix. > test-term-directed.R: Maximizing the pseudolikelihood. > test-term-directed.R: Finished MPLE. > test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-directed.R: Obtaining the responsible dyads. > test-term-directed.R: Evaluating the predictor and response matrix. > test-term-directed.R: Maximizing the pseudolikelihood. > test-term-directed.R: Finished MPLE. > test-term-bipartite.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-bipartite.R: Obtaining the responsible dyads. > test-term-bipartite.R: Evaluating the predictor and response matrix. > test-term-bipartite.R: Maximizing the pseudolikelihood. > test-term-bipartite.R: Finished MPLE. > test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-directed.R: Obtaining the responsible dyads. > test-term-directed.R: Evaluating the predictor and response matrix. > test-term-directed.R: Maximizing the pseudolikelihood. > test-term-directed.R: Finished MPLE. > test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-directed.R: Obtaining the responsible dyads. > test-term-directed.R: Evaluating the predictor and response matrix. > test-term-directed.R: Maximizing the pseudolikelihood. > test-term-directed.R: Finished MPLE. > test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-directed.R: Obtaining the responsible dyads. > test-term-directed.R: Evaluating the predictor and response matrix. > test-term-directed.R: Maximizing the pseudolikelihood. > test-term-directed.R: Finished MPLE. > test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-directed.R: Obtaining the responsible dyads. > test-term-directed.R: Evaluating the predictor and response matrix. > test-term-directed.R: Maximizing the pseudolikelihood. > test-term-directed.R: Finished MPLE. > test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-directed.R: Obtaining the responsible dyads. > test-term-directed.R: Evaluating the predictor and response matrix. > test-term-directed.R: Maximizing the pseudolikelihood. > test-term-directed.R: Finished MPLE. > test-term-directed.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-directed.R: Obtaining the responsible dyads. > test-term-directed.R: Evaluating the predictor and response matrix. > test-term-directed.R: Maximizing the pseudolikelihood. > test-term-directed.R: Finished MPLE. > test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-flexible.R: Obtaining the responsible dyads. > test-term-flexible.R: Evaluating the predictor and response matrix. > test-term-flexible.R: Maximizing the pseudolikelihood. > test-term-flexible.R: Finished MPLE. > test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-flexible.R: Obtaining the responsible dyads. > test-term-flexible.R: Evaluating the predictor and response matrix. > test-term-flexible.R: Maximizing the pseudolikelihood. > test-term-flexible.R: Finished MPLE. > test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-gw-sp.R: Obtaining the responsible dyads. > test-term-gw-sp.R: Evaluating the predictor and response matrix. > test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-flexible.R: Obtaining the responsible dyads. > test-term-flexible.R: Evaluating the predictor and response matrix. > test-term-flexible.R: Maximizing the pseudolikelihood. > test-term-flexible.R: Finished MPLE. > test-term-gw-sp.R: Maximizing the pseudolikelihood. > test-term-gw-sp.R: Finished MPLE. > test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-flexible.R: Obtaining the responsible dyads. > test-term-flexible.R: Evaluating the predictor and response matrix. > test-term-flexible.R: Maximizing the pseudolikelihood. > test-term-flexible.R: Finished MPLE. > test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-flexible.R: Obtaining the responsible dyads. > test-term-flexible.R: Evaluating the predictor and response matrix. > test-term-flexible.R: Maximizing the pseudolikelihood. > test-term-flexible.R: Finished MPLE. > test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-flexible.R: Obtaining the responsible dyads. > test-term-flexible.R: Evaluating the predictor and response matrix. > test-term-flexible.R: Maximizing the pseudolikelihood. > test-term-flexible.R: Finished MPLE. > test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-flexible.R: Obtaining the responsible dyads. > test-term-flexible.R: Evaluating the predictor and response matrix. > test-term-flexible.R: Maximizing the pseudolikelihood. > test-term-flexible.R: Finished MPLE. > test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-flexible.R: Obtaining the responsible dyads. > test-term-flexible.R: Evaluating the predictor and response matrix. > test-term-flexible.R: Maximizing the pseudolikelihood. > test-term-flexible.R: Finished MPLE. > test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-flexible.R: Obtaining the responsible dyads. > test-term-flexible.R: Evaluating the predictor and response matrix. > test-term-flexible.R: Maximizing the pseudolikelihood. > test-term-flexible.R: Finished MPLE. > test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-gw-sp.R: Obtaining the responsible dyads. > test-term-gw-sp.R: Evaluating the predictor and response matrix. > test-term-gw-sp.R: Maximizing the pseudolikelihood. > test-term-gw-sp.R: Finished MPLE. > test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-flexible.R: Obtaining the responsible dyads. > test-term-flexible.R: Evaluating the predictor and response matrix. > test-term-flexible.R: Maximizing the pseudolikelihood. > test-term-flexible.R: Finished MPLE. > test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-flexible.R: Obtaining the responsible dyads. > test-term-flexible.R: Evaluating the predictor and response matrix. > test-term-flexible.R: Maximizing the pseudolikelihood. > test-term-flexible.R: Finished MPLE. > test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-flexible.R: Obtaining the responsible dyads. > test-term-flexible.R: Evaluating the predictor and response matrix. > test-term-flexible.R: Maximizing the pseudolikelihood. > test-term-flexible.R: Finished MPLE. > test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-flexible.R: Obtaining the responsible dyads. > test-term-flexible.R: Evaluating the predictor and response matrix. > test-term-flexible.R: Maximizing the pseudolikelihood. > test-term-flexible.R: Finished MPLE. > test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-gw-sp.R: Obtaining the responsible dyads. > test-term-gw-sp.R: Evaluating the predictor and response matrix. > test-term-gw-sp.R: Maximizing the pseudolikelihood. > test-term-gw-sp.R: Finished MPLE. > test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-flexible.R: Obtaining the responsible dyads. > test-term-flexible.R: Evaluating the predictor and response matrix. > test-term-flexible.R: Maximizing the pseudolikelihood. > test-term-flexible.R: Finished MPLE. > test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-flexible.R: Obtaining the responsible dyads. > test-term-flexible.R: Evaluating the predictor and response matrix. > test-term-flexible.R: Maximizing the pseudolikelihood. > test-term-flexible.R: Finished MPLE. > test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-flexible.R: Obtaining the responsible dyads. > test-term-flexible.R: Evaluating the predictor and response matrix. > test-term-flexible.R: Maximizing the pseudolikelihood. > test-term-flexible.R: Finished MPLE. > test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-flexible.R: Obtaining the responsible dyads. > test-term-flexible.R: Evaluating the predictor and response matrix. > test-term-flexible.R: Maximizing the pseudolikelihood. > test-term-flexible.R: Finished MPLE. > test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-gw-sp.R: Obtaining the responsible dyads. > test-term-gw-sp.R: Evaluating the predictor and response matrix. > test-term-gw-sp.R: Maximizing the pseudolikelihood. > test-term-gw-sp.R: Finished MPLE. > test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-flexible.R: Obtaining the responsible dyads. > test-term-flexible.R: Evaluating the predictor and response matrix. > test-term-flexible.R: Maximizing the pseudolikelihood. > test-term-flexible.R: Finished MPLE. > test-term-flexible.R: Observed statistic(s) edgecov.YearsTrusted are at their greatest attainable values. Their coefficients will be fixed at +Inf. > test-term-flexible.R: All terms are either offsets or extreme values. No optimization is performed. > test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-flexible.R: Obtaining the responsible dyads. > test-term-flexible.R: Evaluating the predictor and response matrix. > test-term-flexible.R: Maximizing the pseudolikelihood. > test-term-flexible.R: Finished MPLE. > test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-gw-sp.R: Obtaining the responsible dyads. > test-term-gw-sp.R: Evaluating the predictor and response matrix. > test-term-gw-sp.R: Maximizing the pseudolikelihood. > test-term-gw-sp.R: Finished MPLE. > test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-flexible.R: Obtaining the responsible dyads. > test-term-flexible.R: Evaluating the predictor and response matrix. > test-term-flexible.R: Maximizing the pseudolikelihood. > test-term-flexible.R: Finished MPLE. > test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-flexible.R: Obtaining the responsible dyads. > test-term-flexible.R: Evaluating the predictor and response matrix. > test-term-flexible.R: Maximizing the pseudolikelihood. > test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-gw-sp.R: Obtaining the responsible dyads. > test-term-gw-sp.R: Evaluating the predictor and response matrix. > test-term-flexible.R: Finished MPLE. > test-term-gw-sp.R: Maximizing the pseudolikelihood. > test-term-gw-sp.R: Finished MPLE. > test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-flexible.R: Obtaining the responsible dyads. > test-term-flexible.R: Evaluating the predictor and response matrix. > test-term-flexible.R: Maximizing the pseudolikelihood. > test-term-flexible.R: Finished MPLE. > test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-flexible.R: Obtaining the responsible dyads. > test-term-flexible.R: Evaluating the predictor and response matrix. > test-term-flexible.R: Maximizing the pseudolikelihood. > test-term-flexible.R: Finished MPLE. > test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-flexible.R: Obtaining the responsible dyads. > test-term-flexible.R: Evaluating the predictor and response matrix. > test-term-flexible.R: Maximizing the pseudolikelihood. > test-term-flexible.R: Finished MPLE. > test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-flexible.R: Obtaining the responsible dyads. > test-term-flexible.R: Evaluating the predictor and response matrix. > test-term-flexible.R: Maximizing the pseudolikelihood. > test-term-flexible.R: Finished MPLE. > test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-flexible.R: Obtaining the responsible dyads. > test-term-flexible.R: Evaluating the predictor and response matrix. > test-term-flexible.R: Maximizing the pseudolikelihood. > test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-gw-sp.R: Obtaining the responsible dyads. > test-term-gw-sp.R: Evaluating the predictor and response matrix. > test-term-flexible.R: Finished MPLE. > test-term-gw-sp.R: Maximizing the pseudolikelihood. > test-term-gw-sp.R: Finished MPLE. > test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-flexible.R: Obtaining the responsible dyads. > test-term-flexible.R: Evaluating the predictor and response matrix. > test-term-flexible.R: Maximizing the pseudolikelihood. > test-term-flexible.R: Finished MPLE. > test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-flexible.R: Obtaining the responsible dyads. > test-term-flexible.R: Evaluating the predictor and response matrix. > test-term-flexible.R: Maximizing the pseudolikelihood. > test-term-flexible.R: Finished MPLE. > test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-flexible.R: Obtaining the responsible dyads. > test-term-flexible.R: Evaluating the predictor and response matrix. > test-term-flexible.R: Maximizing the pseudolikelihood. > test-term-flexible.R: Finished MPLE. > test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-flexible.R: Obtaining the responsible dyads. > test-term-flexible.R: Evaluating the predictor and response matrix. > test-term-flexible.R: Maximizing the pseudolikelihood. > test-term-flexible.R: Finished MPLE. > test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-gw-sp.R: Obtaining the responsible dyads. > test-term-gw-sp.R: Evaluating the predictor and response matrix. > test-term-gw-sp.R: Maximizing the pseudolikelihood. > test-term-gw-sp.R: Finished MPLE. > test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-flexible.R: Obtaining the responsible dyads. > test-term-flexible.R: Evaluating the predictor and response matrix. > test-term-flexible.R: Maximizing the pseudolikelihood. > test-term-flexible.R: Finished MPLE. > test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-flexible.R: Obtaining the responsible dyads. > test-term-flexible.R: Evaluating the predictor and response matrix. > test-term-flexible.R: Maximizing the pseudolikelihood. > test-term-flexible.R: Finished MPLE. > test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-flexible.R: Obtaining the responsible dyads. > test-term-flexible.R: Evaluating the predictor and response matrix. > test-term-flexible.R: Maximizing the pseudolikelihood. > test-term-flexible.R: Finished MPLE. > test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-flexible.R: Obtaining the responsible dyads. > test-term-flexible.R: Evaluating the predictor and response matrix. > test-term-flexible.R: Maximizing the pseudolikelihood. > test-term-flexible.R: Finished MPLE. > test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-gw-sp.R: Obtaining the responsible dyads. > test-term-gw-sp.R: Evaluating the predictor and response matrix. > test-term-gw-sp.R: Maximizing the pseudolikelihood. > test-term-gw-sp.R: Finished MPLE. > test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-flexible.R: Obtaining the responsible dyads. > test-term-flexible.R: Evaluating the predictor and response matrix. > test-term-flexible.R: Maximizing the pseudolikelihood. > test-term-flexible.R: Finished MPLE. > test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-flexible.R: Obtaining the responsible dyads. > test-term-flexible.R: Evaluating the predictor and response matrix. > test-term-flexible.R: Maximizing the pseudolikelihood. > test-term-flexible.R: Finished MPLE. > test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-flexible.R: Obtaining the responsible dyads. > test-term-flexible.R: Evaluating the predictor and response matrix. > test-term-flexible.R: Maximizing the pseudolikelihood. > test-term-flexible.R: Finished MPLE. > test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-flexible.R: Obtaining the responsible dyads. > test-term-flexible.R: Evaluating the predictor and response matrix. > test-term-flexible.R: Maximizing the pseudolikelihood. > test-term-flexible.R: Finished MPLE. > test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-flexible.R: Obtaining the responsible dyads. > test-term-flexible.R: Evaluating the predictor and response matrix. > test-term-flexible.R: Maximizing the pseudolikelihood. > test-term-flexible.R: Finished MPLE. > test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-flexible.R: Obtaining the responsible dyads. > test-term-flexible.R: Evaluating the predictor and response matrix. > test-term-flexible.R: Maximizing the pseudolikelihood. > test-term-flexible.R: Finished MPLE. > test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-gw-sp.R: Obtaining the responsible dyads. > test-term-gw-sp.R: Evaluating the predictor and response matrix. > test-term-gw-sp.R: Maximizing the pseudolikelihood. > test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-flexible.R: Obtaining the responsible dyads. > test-term-flexible.R: Evaluating the predictor and response matrix. > test-term-flexible.R: Maximizing the pseudolikelihood. > test-term-gw-sp.R: Finished MPLE. > test-term-flexible.R: Finished MPLE. > test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-flexible.R: Obtaining the responsible dyads. > test-term-flexible.R: Evaluating the predictor and response matrix. > test-term-flexible.R: Maximizing the pseudolikelihood. > test-term-flexible.R: Finished MPLE. > test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-flexible.R: Obtaining the responsible dyads. > test-term-flexible.R: Evaluating the predictor and response matrix. > test-term-flexible.R: Maximizing the pseudolikelihood. > test-term-flexible.R: Finished MPLE. > test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-flexible.R: Obtaining the responsible dyads. > test-term-flexible.R: Evaluating the predictor and response matrix. > test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-gw-sp.R: Obtaining the responsible dyads. > test-term-gw-sp.R: Evaluating the predictor and response matrix. > test-term-flexible.R: Maximizing the pseudolikelihood. > test-term-flexible.R: Finished MPLE. > test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-flexible.R: Obtaining the responsible dyads. > test-term-flexible.R: Evaluating the predictor and response matrix. > test-term-flexible.R: Maximizing the pseudolikelihood. > test-term-flexible.R: Finished MPLE. > test-term-flexible.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-flexible.R: Obtaining the responsible dyads. > test-term-flexible.R: Evaluating the predictor and response matrix. > test-term-gw-sp.R: Maximizing the pseudolikelihood. > test-term-flexible.R: Maximizing the pseudolikelihood. > test-term-flexible.R: Finished MPLE. > test-term-gw-sp.R: Finished MPLE. > test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-gw-sp.R: Obtaining the responsible dyads. > test-term-gw-sp.R: Evaluating the predictor and response matrix. > test-term-gw-sp.R: Maximizing the pseudolikelihood. > test-term-gw-sp.R: Finished MPLE. > test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-gw-sp.R: Obtaining the responsible dyads. > test-term-gw-sp.R: Evaluating the predictor and response matrix. > test-term-gw-sp.R: Maximizing the pseudolikelihood. > test-term-gw-sp.R: Finished MPLE. > test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-gw-sp.R: Obtaining the responsible dyads. > test-term-gw-sp.R: Evaluating the predictor and response matrix. > test-term-gw-sp.R: Maximizing the pseudolikelihood. > test-term-gw-sp.R: Finished MPLE. > test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-gw-sp.R: Obtaining the responsible dyads. > test-term-gw-sp.R: Evaluating the predictor and response matrix. > test-term-gw-sp.R: Maximizing the pseudolikelihood. > test-term-gw-sp.R: Finished MPLE. > test-term-mm.R: Note: Term 'mm(~Grade >= 10, levels = -1)' skipped because it contributes no statistics. > test-term-mm.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-mm.R: Obtaining the responsible dyads. > test-term-mm.R: Evaluating the predictor and response matrix. > test-term-mm.R: Maximizing the pseudolikelihood. > test-term-mm.R: Finished MPLE. > test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-gw-sp.R: Obtaining the responsible dyads. > test-term-gw-sp.R: Evaluating the predictor and response matrix. > test-term-gw-sp.R: Maximizing the pseudolikelihood. > test-term-gw-sp.R: Finished MPLE. > test-term-mm.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-mm.R: Obtaining the responsible dyads. > test-term-mm.R: Evaluating the predictor and response matrix. > test-term-mm.R: Maximizing the pseudolikelihood. > test-term-mm.R: Finished MPLE. > test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-gw-sp.R: Obtaining the responsible dyads. > test-term-gw-sp.R: Evaluating the predictor and response matrix. > test-term-gw-sp.R: Maximizing the pseudolikelihood. > test-term-gw-sp.R: Finished MPLE. > test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-gw-sp.R: Obtaining the responsible dyads. > test-term-gw-sp.R: Evaluating the predictor and response matrix. > test-term-gw-sp.R: Maximizing the pseudolikelihood. > test-term-mm.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-mm.R: Obtaining the responsible dyads. > test-term-mm.R: Evaluating the predictor and response matrix. > test-term-gw-sp.R: Finished MPLE. > test-term-mm.R: Maximizing the pseudolikelihood. > test-term-mm.R: Finished MPLE. > test-term-mm.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-mm.R: Obtaining the responsible dyads. > test-term-mm.R: Evaluating the predictor and response matrix. > test-term-mm.R: Maximizing the pseudolikelihood. > test-term-mm.R: Finished MPLE. > test-term-options.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-options.R: Obtaining the responsible dyads. > test-term-options.R: Evaluating the predictor and response matrix. > test-term-options.R: Maximizing the pseudolikelihood. > test-term-options.R: Finished MPLE. > test-term-options.R: Evaluating log-likelihood at the estimate. > test-term-options.R: > test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-gw-sp.R: Obtaining the responsible dyads. > test-term-gw-sp.R: Evaluating the predictor and response matrix. > test-term-options.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-options.R: Obtaining the responsible dyads. > test-term-options.R: Evaluating the predictor and response matrix. > test-term-options.R: Maximizing the pseudolikelihood. > test-term-options.R: Finished MPLE. > test-term-options.R: Evaluating log-likelihood at the estimate. > test-term-options.R: > test-term-gw-sp.R: Maximizing the pseudolikelihood. > test-term-gw-sp.R: Finished MPLE. > test-term-options.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-options.R: Obtaining the responsible dyads. > test-term-options.R: Evaluating the predictor and response matrix. > test-term-options.R: Maximizing the pseudolikelihood. > test-term-options.R: Finished MPLE. > test-term-options.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-term-options.R: Iteration 1 of at most 60: > test-term-options.R: 1 > test-term-options.R: Optimizing with step length 1.0000. > test-term-options.R: The log-likelihood improved by 0.0013. > test-term-options.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-term-options.R: Finished MCMLE. > test-term-options.R: Evaluating log-likelihood at the estimate. > test-term-options.R: Fitting the dyad-independent submodel... > test-term-options.R: Bridging between the dyad-independent submodel and the full model... > test-term-options.R: Setting up bridge sampling... > test-term-options.R: Using 16 bridges: 1 > test-term-options.R: 2 > test-term-options.R: 3 > test-term-options.R: 4 > test-term-options.R: 5 > test-term-options.R: 6 > test-term-options.R: 7 > test-term-options.R: 8 > test-term-options.R: 9 > test-term-options.R: 10 > test-term-options.R: 11 > test-term-options.R: 12 > test-term-options.R: 13 > test-term-options.R: 14 > test-term-options.R: 15 > test-term-options.R: 16 > test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-gw-sp.R: Obtaining the responsible dyads. > test-term-gw-sp.R: Evaluating the predictor and response matrix. > test-term-gw-sp.R: Maximizing the pseudolikelihood. > test-term-gw-sp.R: Finished MPLE. > test-term-options.R: . > test-term-options.R: Bridging finished. > test-term-options.R: > test-term-options.R: This model was fit using MCMC. To examine model diagnostics and check > test-term-options.R: for degeneracy, use the mcmc.diagnostics() function. > test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-gw-sp.R: Obtaining the responsible dyads. > test-term-gw-sp.R: Evaluating the predictor and response matrix. > test-term-gw-sp.R: Maximizing the pseudolikelihood. > test-term-gw-sp.R: Finished MPLE. > test-term-options.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-options.R: Obtaining the responsible dyads. > test-term-options.R: Evaluating the predictor and response matrix. > test-term-options.R: Maximizing the pseudolikelihood. > test-term-options.R: Finished MPLE. > test-term-options.R: Starting Monte Carlo maximum likelihood estimation (MCMLE): > test-term-options.R: Iteration 1 of at most 60: > test-term-options.R: 1 > test-term-options.R: Optimizing with step length 1.0000. > test-term-options.R: The log-likelihood improved by 0.0003. > test-term-options.R: Convergence test p-value: < 0.0001. Converged with 99% confidence. > test-term-options.R: Finished MCMLE. > test-term-options.R: This model was fit using MCMC. To examine model diagnostics and check > test-term-options.R: for degeneracy, use the mcmc.diagnostics() function. > test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-gw-sp.R: Obtaining the responsible dyads. > test-term-gw-sp.R: Evaluating the predictor and response matrix. > test-term-gw-sp.R: Maximizing the pseudolikelihood. > test-term-gw-sp.R: Finished MPLE. > test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-undirected.R: Obtaining the responsible dyads. > test-term-undirected.R: Evaluating the predictor and response matrix. > test-term-undirected.R: Maximizing the pseudolikelihood. > test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-gw-sp.R: Obtaining the responsible dyads. > test-term-gw-sp.R: Evaluating the predictor and response matrix. > test-term-gw-sp.R: Maximizing the pseudolikelihood. > test-term-undirected.R: Finished MPLE. > test-term-gw-sp.R: Finished MPLE. > test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-undirected.R: Obtaining the responsible dyads. > test-term-undirected.R: Evaluating the predictor and response matrix. > test-term-undirected.R: Maximizing the pseudolikelihood. > test-term-undirected.R: Finished MPLE. > test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-undirected.R: Obtaining the responsible dyads. > test-term-undirected.R: Evaluating the predictor and response matrix. > test-term-undirected.R: Maximizing the pseudolikelihood. > test-term-undirected.R: Finished MPLE. > test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-gw-sp.R: Obtaining the responsible dyads. > test-term-gw-sp.R: Evaluating the predictor and response matrix. > test-term-gw-sp.R: Maximizing the pseudolikelihood. > test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-undirected.R: Obtaining the responsible dyads. > test-term-undirected.R: Evaluating the predictor and response matrix. > test-term-gw-sp.R: Finished MPLE. > test-term-undirected.R: Maximizing the pseudolikelihood. > test-term-undirected.R: Finished MPLE. > test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-undirected.R: Obtaining the responsible dyads. > test-term-undirected.R: Evaluating the predictor and response matrix. > test-term-undirected.R: Maximizing the pseudolikelihood. > test-term-undirected.R: Finished MPLE. > test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-undirected.R: Obtaining the responsible dyads. > test-term-undirected.R: Evaluating the predictor and response matrix. > test-term-undirected.R: Maximizing the pseudolikelihood. > test-term-undirected.R: Finished MPLE. > test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-undirected.R: Obtaining the responsible dyads. > test-term-undirected.R: Evaluating the predictor and response matrix. > test-term-undirected.R: Maximizing the pseudolikelihood. > test-term-undirected.R: Finished MPLE. > test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-undirected.R: Obtaining the responsible dyads. > test-term-undirected.R: Evaluating the predictor and response matrix. > test-term-undirected.R: Maximizing the pseudolikelihood. > test-term-undirected.R: Finished MPLE. > test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-undirected.R: Obtaining the responsible dyads. > test-term-undirected.R: Evaluating the predictor and response matrix. > test-term-undirected.R: Maximizing the pseudolikelihood. > test-term-undirected.R: Finished MPLE. > test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-gw-sp.R: Obtaining the responsible dyads. > test-term-gw-sp.R: Evaluating the predictor and response matrix. > test-term-gw-sp.R: Maximizing the pseudolikelihood. > test-term-gw-sp.R: Finished MPLE. > test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-undirected.R: Obtaining the responsible dyads. > test-term-undirected.R: Evaluating the predictor and response matrix. > test-term-undirected.R: Maximizing the pseudolikelihood. > test-term-undirected.R: Finished MPLE. > test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-undirected.R: Obtaining the responsible dyads. > test-term-undirected.R: Evaluating the predictor and response matrix. > test-term-undirected.R: Maximizing the pseudolikelihood. > test-term-undirected.R: Finished MPLE. > test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-undirected.R: Obtaining the responsible dyads. > test-term-undirected.R: Evaluating the predictor and response matrix. > test-term-undirected.R: Maximizing the pseudolikelihood. > test-term-undirected.R: Finished MPLE. > test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-undirected.R: Obtaining the responsible dyads. > test-term-undirected.R: Evaluating the predictor and response matrix. > test-term-undirected.R: Maximizing the pseudolikelihood. > test-term-undirected.R: Finished MPLE. > test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-gw-sp.R: Obtaining the responsible dyads. > test-term-gw-sp.R: Evaluating the predictor and response matrix. > test-term-gw-sp.R: Maximizing the pseudolikelihood. > test-term-gw-sp.R: Finished MPLE. > test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-undirected.R: Obtaining the responsible dyads. > test-term-undirected.R: Evaluating the predictor and response matrix. > test-term-undirected.R: Maximizing the pseudolikelihood. > test-term-undirected.R: Finished MPLE. > test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-undirected.R: Obtaining the responsible dyads. > test-term-undirected.R: Evaluating the predictor and response matrix. > test-term-undirected.R: Maximizing the pseudolikelihood. > test-term-undirected.R: Finished MPLE. > test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-undirected.R: Obtaining the responsible dyads. > test-term-undirected.R: Evaluating the predictor and response matrix. > test-term-undirected.R: Maximizing the pseudolikelihood. > test-term-undirected.R: Finished MPLE. > test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-undirected.R: Obtaining the responsible dyads. > test-term-undirected.R: Evaluating the predictor and response matrix. > test-term-undirected.R: Maximizing the pseudolikelihood. > test-term-undirected.R: Finished MPLE. > test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-gw-sp.R: Obtaining the responsible dyads. > test-term-gw-sp.R: Evaluating the predictor and response matrix. > test-term-gw-sp.R: Maximizing the pseudolikelihood. > test-term-gw-sp.R: Finished MPLE. > test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-undirected.R: Obtaining the responsible dyads. > test-term-undirected.R: Evaluating the predictor and response matrix. > test-term-undirected.R: Maximizing the pseudolikelihood. > test-term-undirected.R: Finished MPLE. > test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-undirected.R: Obtaining the responsible dyads. > test-term-undirected.R: Evaluating the predictor and response matrix. > test-term-undirected.R: Maximizing the pseudolikelihood. > test-term-undirected.R: Finished MPLE. > test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-undirected.R: Obtaining the responsible dyads. > test-term-undirected.R: Evaluating the predictor and response matrix. > test-term-undirected.R: Maximizing the pseudolikelihood. > test-term-undirected.R: Finished MPLE. > test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-gw-sp.R: Obtaining the responsible dyads. > test-term-gw-sp.R: Evaluating the predictor and response matrix. > test-term-gw-sp.R: Maximizing the pseudolikelihood. > test-term-gw-sp.R: Finished MPLE. > test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-undirected.R: Obtaining the responsible dyads. > test-term-undirected.R: Evaluating the predictor and response matrix. > test-term-undirected.R: Maximizing the pseudolikelihood. > test-term-undirected.R: Finished MPLE. > test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-undirected.R: Obtaining the responsible dyads. > test-term-undirected.R: Evaluating the predictor and response matrix. > test-term-undirected.R: Maximizing the pseudolikelihood. > test-term-undirected.R: Finished MPLE. > test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-gw-sp.R: Obtaining the responsible dyads. > test-term-gw-sp.R: Evaluating the predictor and response matrix. > test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-undirected.R: Obtaining the responsible dyads. > test-term-undirected.R: Evaluating the predictor and response matrix. > test-term-undirected.R: Maximizing the pseudolikelihood. > test-term-undirected.R: Finished MPLE. > test-term-gw-sp.R: Maximizing the pseudolikelihood. > test-term-gw-sp.R: Finished MPLE. > test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-undirected.R: Obtaining the responsible dyads. > test-term-undirected.R: Evaluating the predictor and response matrix. > test-term-undirected.R: Maximizing the pseudolikelihood. > test-term-undirected.R: Finished MPLE. > test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-undirected.R: Obtaining the responsible dyads. > test-term-undirected.R: Evaluating the predictor and response matrix. > test-term-undirected.R: Maximizing the pseudolikelihood. > test-term-undirected.R: Finished MPLE. > test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-undirected.R: Obtaining the responsible dyads. > test-term-undirected.R: Evaluating the predictor and response matrix. > test-term-undirected.R: Maximizing the pseudolikelihood. > test-term-undirected.R: Finished MPLE. > test-term-undirected.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-undirected.R: Obtaining the responsible dyads. > test-term-undirected.R: Evaluating the predictor and response matrix. > test-term-undirected.R: Maximizing the pseudolikelihood. > test-term-undirected.R: Finished MPLE. > test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-gw-sp.R: Obtaining the responsible dyads. > test-term-gw-sp.R: Evaluating the predictor and response matrix. > test-term-gw-sp.R: Maximizing the pseudolikelihood. > test-term-gw-sp.R: Finished MPLE. > test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-gw-sp.R: Obtaining the responsible dyads. > test-term-gw-sp.R: Evaluating the predictor and response matrix. > test-term-gw-sp.R: Maximizing the pseudolikelihood. > test-term-gw-sp.R: Finished MPLE. > test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-gw-sp.R: Obtaining the responsible dyads. > test-term-gw-sp.R: Evaluating the predictor and response matrix. > test-term-gw-sp.R: Maximizing the pseudolikelihood. > test-term-gw-sp.R: Finished MPLE. > test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-gw-sp.R: Obtaining the responsible dyads. > test-term-gw-sp.R: Evaluating the predictor and response matrix. > test-term-gw-sp.R: Maximizing the pseudolikelihood. > test-term-gw-sp.R: Finished MPLE. > test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-gw-sp.R: Obtaining the responsible dyads. > test-term-gw-sp.R: Evaluating the predictor and response matrix. > test-term-gw-sp.R: Maximizing the pseudolikelihood. > test-term-gw-sp.R: Finished MPLE. > test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-gw-sp.R: Obtaining the responsible dyads. > test-term-gw-sp.R: Evaluating the predictor and response matrix. > test-term-gw-sp.R: Maximizing the pseudolikelihood. > test-term-gw-sp.R: Finished MPLE. > test-term-gw-sp.R: Starting maximum pseudolikelihood estimation (MPLE): > test-term-gw-sp.R: Obtaining the responsible dyads. > test-term-gw-sp.R: Evaluating the predictor and response matrix. > test-term-gw-sp.R: Maximizing the pseudolikelihood. > test-term-gw-sp.R: Finished MPLE. > test-u-function.R: Best valid proposal 'CondDegree' cannot take into account hint(s) 'triadic'. > test-u-function.R: Best valid proposal 'CondDegree' cannot take into account hint(s) 'triadic'. > test-u-function.R: Best valid proposal 'CondDegree' cannot take into account hint(s) 'triadic'. > test-u-function.R: Best valid proposal 'DiscUnif2' cannot take into account hint(s) 'sparse' and 'triadic'. > test-u-function.R: Best valid proposal 'DiscTNT' cannot take into account hint(s) 'triadic'. > test-valued-sim.R: mean=1, var=4, corr=0.3 > test-valued-sim.R: eta=(0.192307692307692,0.0824175824175824,0.362637362637363) > test-valued-sim.R: Best valid proposal 'StdNormal' cannot take into account hint(s) 'sparse' and 'triadic'. > test-valued-sim.R: Simulated mean (stats only):0.9964445 > test-valued-sim.R: Best valid proposal 'StdNormal' cannot take into account hint(s) 'sparse' and 'triadic'. > test-valued-sim.R: Simulated means (target=1): > test-valued-sim.R: [,1] [,2] [,3] > test-valued-sim.R: [1,] NA 1.0034478 0.9683903 > test-valued-sim.R: [2,] 1.0302079 NA 0.8923730 > test-valued-sim.R: [3,] 0.6870641 0.7628159 NA > test-valued-sim.R: Simulated vars (target=4): > test-valued-sim.R: [,1] [,2] [,3] > test-valued-sim.R: [1,] NA 3.863045 4.034701 > test-valued-sim.R: [2,] 3.944388 NA 3.888997 > test-valued-sim.R: [3,] 3.740890 4.202553 NA > test-valued-sim.R: Simulated correlations (1,2) (1,3) (2,3) (target=0.3): > test-valued-sim.R: [1] 0.2781206 0.2324470 0.3420405 > test-valued-sim.R: ==== output='stats', coef=2.380183 > test-valued-sim.R: Best valid proposal 'DiscUnif' cannot take into account hint(s) 'sparse' and 'triadic'. > test-valued-sim.R: ==== output='network', coef=2.380183 > test-valued-sim.R: Best valid proposal 'DiscUnif' cannot take into account hint(s) 'sparse' and 'triadic'. > test-valued-sim.R: ==== output='stats', coef=0 > test-valued-sim.R: Best valid proposal 'DiscUnif' cannot take into account hint(s) 'sparse' and 'triadic'. > test-valued-sim.R: ==== output='network', coef=0 > test-valued-sim.R: Best valid proposal 'DiscUnif' cannot take into account hint(s) 'sparse' and 'triadic'. > test-valued-sim.R: ==== output='stats', coef=2.8858 > test-valued-sim.R: Best valid proposal 'Unif' cannot take into account hint(s) 'sparse' and 'triadic'. > test-valued-sim.R: ==== output='network', coef=2.8858 > test-valued-sim.R: Best valid proposal 'Unif' cannot take into account hint(s) 'sparse' and 'triadic'. > test-valued-sim.R: ==== output='stats', coef=0 > test-valued-sim.R: Best valid proposal 'Unif' cannot take into account hint(s) 'sparse' and 'triadic'. > test-valued-sim.R: ==== output='network', coef=0 > test-valued-sim.R: Best valid proposal 'Unif' cannot take into account hint(s) 'sparse' and 'triadic'. > test-valued-sim.R: > test-valued-sim.R: 'ergm.count' 4.1.3 (2025-09-10), part of the Statnet Project > test-valued-sim.R: * 'news(package="ergm.count")' for changes since last version > test-valued-sim.R: * 'citation("ergm.count")' for citation information > test-valued-sim.R: * 'https://statnet.org' for help, support, and other information > test-valued-sim.R: > test-valued-terms.R: Best valid proposal 'DiscUnif' cannot take into account hint(s) 'sparse' and 'triadic'. > test-valued-terms.R: Best valid proposal 'DiscUnif' cannot take into account hint(s) 'sparse' and 'triadic'. > test-valued-terms.R: Best valid proposal 'DiscUnif' cannot take into account hint(s) 'sparse' and 'triadic'. > test-valued-terms.R: Best valid proposal 'DiscUnif' cannot take into account hint(s) 'sparse' and 'triadic'. > test-valued-terms.R: > test-valued-terms.R: 'ergm.count' 4.1.3 (2025-09-10), part of the Statnet Project > test-valued-terms.R: * 'news(package="ergm.count")' for changes since last version > test-valued-terms.R: * 'citation("ergm.count")' for citation information > test-valued-terms.R: * 'https://statnet.org' for help, support, and other information > test-valued-terms.R: [ FAIL 2 | WARN 0 | SKIP 1 | PASS 4275 ] ══ Skipped tests (1) ═══════════════════════════════════════════════════════════ • empty test (1): ══ Failed tests ════════════════════════════════════════════════════════════════ ── Failure ('test-term-Offset.R:19:3'): Estimation with Offset() operator works ── Expected failure message to match regexp ".* did not throw the expected message.*". Actual message: x | Expected `off <- ergm(nw ~ edges + offset(triangle), offset.coef = 0.1)` to throw a message. ── Failure ('test-term-Offset.R:23:3'): Estimation with Offset() operator works ── Expected failure message to match regexp ".* did not throw the expected message.*". Actual message: x | Expected `Off <- ergm(nw ~ edges + Offset(~triangle, which = 1, coef = 0.1))` to throw a message. [ FAIL 2 | WARN 0 | SKIP 1 | PASS 4275 ] Error: ! Test failures. Execution halted Package: futile.logger Check: tests New result: ERROR Running ‘testthat.R’ [2s/2s] Running the tests in ‘tests/testthat.R’ failed. Complete output: > library(testthat) > library(futile.logger) > test_check("futile.logger") Saving _problems/test_layout-7.R Saving _problems/test_layout-16.R Saving _problems/test_layout-23.R Saving _problems/test_layout-29.R Saving _problems/test_layout-35.R Saving _problems/test_logger-6.R Saving _problems/test_logger-14.R Saving _problems/test_logger-22.R Saving _problems/test_logger-34.R Saving _problems/test_logger-47.R Saving _problems/test_logger-61.R Saving _problems/test_logger-80.R [ FAIL 12 | WARN 1 | SKIP 0 | PASS 40 ] ══ Failed tests ════════════════════════════════════════════════════════════════ ── Error ('test_layout.R:7:3'): Embedded format string ───────────────────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(length(grep("INFO", raw)) > 0, is_true()) at test_layout.R:7:3 ── Error ('test_layout.R:16:3'): Custom layout dereferences level field ──────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that("xxx[INFO]xxx" == raw, is_true()) at test_layout.R:16:3 ── Error ('test_layout.R:23:3'): Raw null value is printed ───────────────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(...) at test_layout.R:23:3 ── Error ('test_layout.R:29:3'): Single null value is printed ────────────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(...) at test_layout.R:29:3 ── Error ('test_layout.R:35:3'): Null is printed amongst variables ───────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(...) at test_layout.R:35:3 ── Error ('test_logger.R:6:3'): Default settings ─────────────────────────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(length(grep("INFO", raw)) > 0, is_true()) at test_logger.R:6:3 ── Error ('test_logger.R:14:3'): Change root threshold ───────────────────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(length(raw) == 0, is_true()) at test_logger.R:14:3 ── Error ('test_logger.R:22:3'): Capture works as expected ───────────────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(length(raw) == 9, is_true()) at test_logger.R:22:3 ── Error ('test_logger.R:34:3'): Create new logger ───────────────────────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(length(raw.root) == 0, is_true()) at test_logger.R:34:3 ── Error ('test_logger.R:47:3'): Hierarchy is honored ────────────────────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(length(raw.root) == 0, is_true()) at test_logger.R:47:3 ── Error ('test_logger.R:61:3'): Hierarchy inheritance ───────────────────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(length(raw.root) == 0, is_true()) at test_logger.R:61:3 ── Error ('test_logger.R:80:3'): carp returns output ─────────────────────────── Error in `is_false()`: could not find function "is_false" Backtrace: ▆ 1. └─testthat::expect_that(flog.carp(), is_false()) at test_logger.R:80:3 [ FAIL 12 | WARN 1 | SKIP 0 | PASS 40 ] Error: ! Test failures. Execution halted Package: gert Check: tests New result: ERROR Running ‘spelling.R’ [0s/0s] Running ‘testthat.R’ [2s/2s] Running the tests in ‘tests/testthat.R’ failed. Complete output: > library(testthat) > library(gert) Linking to libgit2 v1.9.1, ssh support: YES Global config: /home/hornik/.gitconfig Default user: yourname > > test_check("gert") Saving _problems/test-commit-109.R [ FAIL 2 | WARN 0 | SKIP 7 | PASS 34 ] ══ Skipped tests (7) ═══════════════════════════════════════════════════════════ • On CRAN (7): 'test-auth.R:3:3', 'test-auth.R:12:3', 'test-auth.R:32:3', 'test-clone.R:2:3', 'test-rebase.R:2:3', 'test-remotes.R:2:3', 'test-remotes.R:26:3' ══ Failed tests ════════════════════════════════════════════════════════════════ ── Error ('test-commit.R:109:3'): status reports a conflicted file ───────────── Error in `git_branch_checkout("master", repo = repo)`: No local or remote branch 'master' found. Backtrace: ▆ 1. └─gert::git_branch_checkout("master", repo = repo) at test-commit.R:109:3 ── Error ('test-merge.R:18:3'): merge analysis works ─────────────────────────── Error in `libgit2::git_annotated_commit_from_revspec`: revspec 'master' not found Backtrace: ▆ 1. ├─testthat::expect_equal(git_merge_analysis("master"), "fastforward") at test-merge.R:18:3 2. │ └─testthat::quasi_label(enquo(object), label) 3. │ └─rlang::eval_bare(expr, quo_get_env(quo)) 4. ├─gert::git_merge_analysis("master") 5. └─gert:::raise_libgit2_error(...) [ FAIL 2 | WARN 0 | SKIP 7 | PASS 34 ] Error: ! Test failures. Execution halted Package: ggdist Check: tests New result: ERROR Running ‘testthat.R’ [171s/173s] Running the tests in ‘tests/testthat.R’ failed. Complete output: > # This file is part of the standard setup for testthat. > # It is recommended that you do not modify it. > # > # Where should you do additional test configuration? > # Learn more about the roles of various files in: > # * https://r-pkgs.org/testing-design.html#sec-tests-files-overview > # * https://testthat.r-lib.org/articles/special-files.html > > library(testthat) > library(ggdist) # nolint > > test_check("ggdist") Saving _problems/test.scale_-73.R [ FAIL 1 | WARN 0 | SKIP 109 | PASS 721 ] ══ Skipped tests (109) ═════════════════════════════════════════════════════════ • On CRAN (109): 'test.curve_interval.R:12:1', 'test.density.R:82:1', 'test.density.R:149:1', 'test.geom_blur_dots.R:7:1', 'test.geom_dotsinterval.R:13:1', 'test.geom_dotsinterval.R:82:1', 'test.geom_dotsinterval.R:113:1', 'test.geom_dotsinterval.R:144:1', 'test.geom_dotsinterval.R:169:1', 'test.geom_dotsinterval.R:192:1', 'test.geom_dotsinterval.R:212:1', 'test.geom_dotsinterval.R:225:1', 'test.geom_dotsinterval.R:256:1', 'test.geom_dotsinterval.R:297:1', 'test.geom_dotsinterval.R:338:1', 'test.geom_dotsinterval.R:346:1', 'test.geom_dotsinterval.R:365:1', 'test.geom_dotsinterval.R:376:1', 'test.geom_dotsinterval.R:394:1', 'test.geom_dotsinterval.R:408:1', 'test.geom_dotsinterval.R:684:1', 'test.geom_dotsinterval.R:701:1', 'test.geom_interval.R:18:1', 'test.geom_interval.R:60:1', 'test.geom_interval.R:81:1', 'test.geom_lineribbon.R:24:1', 'test.geom_lineribbon.R:45:1', 'test.geom_lineribbon.R:91:1', 'test.geom_lineribbon.R:111:1', 'test.geom_lineribbon.R:150:1', 'test.geom_lineribbon.R:170:1', 'test.geom_lineribbon.R:199:1', 'test.geom_lineribbon.R:216:1', 'test.geom_lineribbon.R:243:1', 'test.geom_lineribbon.R:277:1', 'test.geom_pointinterval.R:17:1', 'test.geom_pointinterval.R:52:1', 'test.geom_pointinterval.R:81:1', 'test.geom_pointinterval.R:111:1', 'test.geom_pointinterval.R:136:1', 'test.geom_slabinterval.R:46:1', 'test.geom_slabinterval.R:70:1', 'test.geom_slabinterval.R:105:1', 'test.geom_slabinterval.R:120:1', 'test.geom_slabinterval.R:233:1', 'test.geom_slabinterval.R:246:1', 'test.geom_slabinterval.R:277:1', 'test.geom_slabinterval.R:292:1', 'test.guide_rampbar.R:12:1', 'test.guide_rampbar.R:31:1', 'test.parse_dist.R:76:1', 'test.point_interval.R:712:1', 'test.position_dodgejust.R:11:1', 'test.position_dodgejust.R:65:1', 'test.scale_.R:12:1', 'test.scale_colour_ramp.R:13:1', 'test.scale_colour_ramp.R:40:1', 'test.scale_colour_ramp.R:75:1', 'test.scale_colour_ramp.R:97:1', 'test.scale_colour_ramp.R:129:1', 'test.scale_thickness.R:12:1', 'test.stat_cdfinterval.R:10:1', 'test.stat_dist_slabinterval.R:14:1', 'test.stat_dist_slabinterval.R:67:1', 'test.stat_dist_slabinterval.R:134:1', 'test.stat_dist_slabinterval.R:178:1', 'test.stat_dist_slabinterval.R:195:1', 'test.stat_dist_slabinterval.R:215:1', 'test.stat_dist_slabinterval.R:238:1', 'test.stat_dist_slabinterval.R:289:1', 'test.stat_dist_slabinterval.R:354:1', 'test.stat_dist_slabinterval.R:428:1', 'test.stat_dist_slabinterval.R:446:1', 'test.stat_dist_slabinterval.R:462:1', 'test.stat_dist_slabinterval.R:479:1', 'test.stat_dist_slabinterval.R:499:1', 'test.stat_dist_slabinterval.R:513:1', 'test.stat_dist_slabinterval.R:529:1', 'test.stat_dist_slabinterval.R:542:1', 'test.stat_dist_slabinterval.R:558:1', 'test.stat_dist_slabinterval.R:598:1', 'test.stat_dist_slabinterval.R:949:1', 'test.stat_dist_slabinterval.R:979:1', 'test.stat_dist_slabinterval.R:1037:1', 'test.stat_dist_slabinterval.R:1069:1', 'test.stat_dist_slabinterval.R:1128:1', 'test.stat_dist_slabinterval.R:1147:1', 'test.stat_eye.R:11:1', 'test.stat_eye.R:38:1', 'test.stat_eye.R:72:1', 'test.stat_sample_slabinterval.R:14:1', 'test.stat_sample_slabinterval.R:37:1', 'test.stat_sample_slabinterval.R:63:1', 'test.stat_sample_slabinterval.R:87:1', 'test.stat_sample_slabinterval.R:107:1', 'test.stat_sample_slabinterval.R:131:1', 'test.stat_sample_slabinterval.R:162:1', 'test.stat_sample_slabinterval.R:181:1', 'test.stat_sample_slabinterval.R:221:1', 'test.stat_sample_slabinterval.R:267:1', 'test.stat_sample_slabinterval.R:288:1', 'test.stat_spike.R:11:1', 'test.subguide.R:12:1', 'test.subguide.R:41:1', 'test.subguide.R:121:1', 'test.subguide.R:164:1', 'test.subguide.R:189:1', 'test.subguide.R:238:1', 'test.theme_ggdist.R:12:1' ══ Failed tests ════════════════════════════════════════════════════════════════ ── Error ('test.scale_.R:71:3'): mapping custom aesthetics works ─────────────── Error in `as_continuous_pal(elem)`: Cannot convert `x` to a continuous palette. Backtrace: ▆ 1. └─vdiffr::expect_doppelganger(...) at test.scale_.R:71:3 2. └─vdiffr (local) writer(fig, testcase, title) 3. ├─vdiffr:::print_plot(plot, title) 4. └─vdiffr:::print_plot.ggplot(plot, title) 5. ├─base::print(p) 6. └─ggplot2 (local) `print.ggplot2::ggplot`(p) 7. ├─ggplot2::ggplot_build(x) 8. └─ggplot2 (local) `ggplot_build.ggplot2::ggplot`(x) 9. └─npscales$set_palettes(plot@theme) 10. └─ggplot2 (local) set_palettes(..., self = self) 11. ├─scales::as_continuous_pal(elem) 12. └─scales:::as_continuous_pal.default(elem) 13. └─cli::cli_abort("Cannot convert {.arg x} to a continuous palette.") 14. └─rlang::abort(...) [ FAIL 1 | WARN 0 | SKIP 109 | PASS 721 ] Deleting unused snapshots: 'test.scale_/dots-slab-color-plus-slab-shape-mapping.svg', 'test.scale_/interval-alpha-continuous-mapping.svg', 'test.scale_/interval-alpha-discrete-mapping.svg', 'test.scale_/interval-color-continuous-mapping.svg', 'test.scale_/interval-color-discrete-mapping.svg', 'test.scale_/interval-linetype-discrete-mapping.svg', 'test.scale_/interval-size-continuous-mapping.svg', 'test.scale_/interval-size-discrete-mapping.svg', 'test.scale_/point-alpha-continuous-mapping.svg', 'test.scale_/point-alpha-discrete-mapping.svg', 'test.scale_/point-fill-continuous-mapping.svg', 'test.scale_/point-fill-discrete-mapping.svg', 'test.scale_/point-size-continuous-mapping.svg', 'test.scale_/point-size-discrete-mapping.svg', 'test.scale_/slab-alpha-continuous-mapping.svg', 'test.scale_/slab-alpha-discrete-mapping.svg', 'test.scale_/slab-color-continuous-mapping.svg', 'test.scale_/slab-color-discrete-mapping.svg', …, 'test.scale_/slab-size-continuous-mapping.svg', and 'test.scale_/slab-size-discrete-mapping.svg' Error: ! Test failures. Execution halted Package: ggseg Check: tests New result: ERROR Running ‘spelling.R’ [0s/0s] Running ‘testthat.R’ [45s/45s] Running the tests in ‘tests/testthat.R’ failed. Complete output: > library(testthat) > > test_check("ggseg") Loading required package: ggseg Saving _problems/test-brain_palettes-10.R merging atlas and data by 'region' merging atlas and data by 'region' [ FAIL 1 | WARN 0 | SKIP 8 | PASS 106 ] ══ Skipped tests (8) ═══════════════════════════════════════════════════════════ • On CRAN (7): 'test-brain-atlas-plots.R:2:1', 'test-ggseg.R:8:1', 'test-ggseg.R:41:1', 'test-ggseg.R:50:1', 'test-ggseg_atlas.R:94:1', 'test-scale_brain.R:2:1', 'test-theme_brain.R:2:1' • empty test (1): 'test-coord-funcs.R:1:1' ══ Failed tests ════════════════════════════════════════════════════════════════ ── Error ('test-brain_palettes.R:10:3'): Check that palette extraction happens ok ── Error in `expect_warning(length(brain_pal("aseg", 2)), 3)`: `regexp` must be a single string, `NA`, or `NULL`, not the number 3. Backtrace: ▆ 1. └─testthat::expect_warning(regexp = 3) at test-brain_palettes.R:10:3 2. └─testthat:::check_string(regexp, allow_null = TRUE, allow_na = TRUE) 3. └─testthat:::stop_input_type(...) 4. └─rlang::abort(message, ..., call = call, arg = arg) [ FAIL 1 | WARN 0 | SKIP 8 | PASS 106 ] Error: ! Test failures. Execution halted Package: graphhopper Check: tests New result: ERROR Running ‘testthat.R’ [2s/2s] Running the tests in ‘tests/testthat.R’ failed. Complete output: > library(testthat) > library(graphhopper) > > test_check("graphhopper") Saving _problems/test_route-22.R [ FAIL 1 | WARN 0 | SKIP 2 | PASS 6 ] ══ Skipped tests (2) ═══════════════════════════════════════════════════════════ • gh_is_avialable() is not TRUE (2): 'test_route-local-gh-instance.R:10:3', 'test_spt-local-gh-instance.R:4:3' ══ Failed tests ════════════════════════════════════════════════════════════════ ── Error ('test_route.R:9:3'): sf LINESTRING ─────────────────────────────────── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test_route.R:9:3 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) [ FAIL 1 | WARN 0 | SKIP 2 | PASS 6 ] Error: ! Test failures. Execution halted Package: greeks Check: tests New result: ERROR Running ‘testthat.R’ [56s/57s] Running the tests in ‘tests/testthat.R’ failed. Complete output: > library(testthat) > library(greeks) > > test_check("greeks") Saving _problems/test-BS_European_Greeks-107.R Saving _problems/test-BS_Geometric_Asian_Greeks-81.R Saving _problems/test-Implied_Volatility-90.R [1] "custom payoff" [1] "custom payoff" [1] "custom payoff" [ FAIL 3 | WARN 0 | SKIP 0 | PASS 17 ] ══ Failed tests ════════════════════════════════════════════════════════════════ ── Error ('test-BS_European_Greeks.R:107:3'): BS_European_Greeks is correct ──── Error in `expect(max(error) < sqrt(epsilon))`: `failure_message` must be a character vector, not absent. Backtrace: ▆ 1. └─testthat::expect(failure_message = ) at test-BS_European_Greeks.R:107:3 2. └─testthat:::check_character(failure_message) 3. └─testthat:::stop_input_type(...) 4. └─rlang::abort(message, ..., call = call, arg = arg) ── Error ('test-BS_Geometric_Asian_Greeks.R:81:3'): BS_Geometric_Asian_Greeks is correct ── Error in `expect(max(error) < sqrt(epsilon))`: `failure_message` must be a character vector, not absent. Backtrace: ▆ 1. └─testthat::expect(failure_message = ) at test-BS_Geometric_Asian_Greeks.R:81:3 2. └─testthat:::check_character(failure_message) 3. └─testthat:::stop_input_type(...) 4. └─rlang::abort(message, ..., call = call, arg = arg) ── Error ('test-Implied_Volatility.R:90:3'): implied volatility is correct ───── Error in `expect(max(abs(option_price_test - option_price)) < 1e-06)`: `failure_message` must be a character vector, not absent. Backtrace: ▆ 1. └─testthat::expect(failure_message = ) at test-Implied_Volatility.R:90:3 2. └─testthat:::check_character(failure_message) 3. └─testthat:::stop_input_type(...) 4. └─rlang::abort(message, ..., call = call, arg = arg) [ FAIL 3 | WARN 0 | SKIP 0 | PASS 17 ] Error: ! Test failures. Execution halted Package: hdcuremodels Check: tests New result: ERROR Running ‘testthat.R’ [291s/292s] Running the tests in ‘tests/testthat.R’ failed. Complete output: > # This file is part of the standard setup for testthat. > # It is recommended that you do not modify it. > # > # Where should you do additional test configuration? > # Learn more about the roles of various files in: > # * https://r-pkgs.org/testing-design.html#sec-tests-files-overview > # * https://testthat.r-lib.org/articles/special-files.html > > library(testthat) > library(hdcuremodels) > > test_check("hdcuremodels") Fold 1 out of 2 training... Fold 2 out of 2 training... Selected lambda for incidence: 0.083 Selected lambda for latency: 0.083 Maximum C-statistic: 0.724278947141186 Fitting a final model... Fold 1 out of 2 training... Fold 2 out of 2 training... Selected lambda for incidence: 0.062 Selected lambda for latency: 0.062 Maximum C-statistic: 0.756490658089751 Fitting a final model... Fold 1 out of 2 training... Fold 2 out of 2 training... Selected lambda for incidence: 0.062 Selected lambda for latency: 0.062 Maximum C-statistic: 0.756012758893876 Fitting a final model... Fold 1 out of 2 training... Fold 2 out of 2 training... Selected step: 297 Maximum C-statistic: 0.770985658659945 Fitting a final model... Fold 1 out of 2 training... Fold 2 out of 2 training... Selected step: 434 Maximum C-statistic: 0.734125636924605 Fitting a final model... Fold 1 out of 2 training... Fold 2 out of 2 training... Selected lambda for incidence: 0.09 Selected lambda for latency: 0.09 Maximum C-statistic: 0.691812421454834 Fitting a final model... Fold 1 out of 2 training... Fold 2 out of 2 training... Selected lambda for incidence: 0.105 Selected lambda for latency: 0.105 Maximum C-statistic: 0.71552008323144 Fitting a final model... Fold 1 out of 2 training... Fold 2 out of 2 training... Selected step: 27 Maximum C-statistic: 0.674984053600791 Fitting a final model... Fold 1 out of 2 training... Fold 2 out of 2 training... Selected step: 3 Maximum C-statistic: 0.697702519726813 Fitting a final model... Family: Cox Algorithm: EM Family: Cox Algorithm: EM Saving _problems/test-formula-11.R Saving _problems/test-logLik-11.R Saving _problems/test-nobs-11.R Fold 1 out of 2 training... Fold 2 out of 2 training... Selected step: 27 Maximum C-statistic: 0.674984053600791 Fitting a final model... Fold 1 out of 2 training... Fold 2 out of 2 training... Selected lambda for incidence: 0.155 Selected lambda for latency: 0.155 Maximum C-statistic: 0.635331032110145 Fitting a final model... Fold 1 out of 2 training... Fold 2 out of 2 training... Selected lambda for incidence: 0.083 Selected lambda for latency: 0.083 Maximum C-statistic: 0.724278947141186 Fitting a final model... mixturecure object fit using Weibull GMIFS algorithm $b_path U1 U2 X1 X2 X3 X4 [1,] 0 0 0.01 0 0 0 [2,] 0 0 0.02 0 0 0 [3,] 0 0 0.03 0 0 0 [4,] 0 0 0.04 0 0 0 [5,] 0 0 0.05 0 0 0 [6,] 0 0 0.06 0 0 0 1274 more rows 6 more columns $beta_path U1 U2 X1 X2 X3 X4 [1,] 0 0 0.01 0 0 0.00 [2,] 0 0 0.02 0 0 0.00 [3,] 0 0 0.03 0 0 0.00 [4,] 0 0 0.03 0 0 0.01 [5,] 0 0 0.03 0 0 0.02 [6,] 0 0 0.03 0 0 0.03 1274 more rows 6 more columns $rate 1.74771 1.747557 1.745463 1.776432 1.806308 1.835547 1274 more elements $alpha 0.5120896 0.5130922 0.5131297 0.5149383 0.5167088 0.51841 1274 more elements Fold 1 out of 2 training... Fold 2 out of 2 training... Selected lambda for incidence: 0.083 Selected lambda for latency: 0.083 Maximum C-statistic: 0.724278947141186 Fitting a final model... mixturecure object fit using Cox EM algorithm $b 0 0 0 0 0 0 6 more elements $beta 0 0 0.8829866 0 0.3677159 0 6 more elements Fold 1 out of 2 training... Fold 2 out of 2 training... Selected lambda for incidence: 0.062 Selected lambda for latency: 0.062 Maximum C-statistic: 0.756490658089751 Fitting a final model... mixturecure object fit using Weibull EM algorithm $b 0 0 0.3534216 -0.1938579 0 -0.4778904 6 more elements $beta 0 0 0 0 0 0 6 more elements $rate 5.125488 $alpha 0.897226 Mixture cure model fit using the EM algorithm Number of non-zero incidence covariates at minimum AIC: 0 Number of non-zero latency covariates at minimum AIC: 17 Optimal step for selected information criterion: EM algorithm at step = 51 logLik = -118.896956094066 at step = 51 AIC = 273.793912188132 at step = 51 mAIC = 490.199530466681 at step = 51 cAIC = 290.4768390174 at step = 51 BIC = 311.492114308129 at step = 51 mBIC = 453.037837086205 at step = 51 EBIC = 370.050413661889 Mixture cure model fit using the EM algorithm Number of non-zero incidence covariates at minimum AIC: 0 Number of non-zero latency covariates at minimum AIC: 17 Optimal step for selected information criterion: EM algorithm at step = 51 logLik = -118.896956094066 at step = 51 AIC = 273.793912188132 at step = 51 mAIC = 490.199530466681 at step = 51 cAIC = 290.4768390174 at step = 51 BIC = 311.492114308129 at step = 51 mBIC = 453.037837086205 at step = 51 EBIC = 370.050413661889 Fold 1 out of 2 training... Fold 2 out of 2 training... Selected lambda for incidence: 0.054 Selected lambda for latency: 0.054 Maximum C-statistic: 0.703222725537594 Fitting a final model... Mixture cure model fit using the EM algorithm using cross-validation Number of non-zero incidence covariates: 3 Number of non-zero latency covariates: 26 Mixture cure model fit using the EM algorithm using cross-validation Number of non-zero incidence covariates: 3 Number of non-zero latency covariates: 26 [ FAIL 3 | WARN 0 | SKIP 0 | PASS 552 ] ══ Failed tests ════════════════════════════════════════════════════════════════ ── Error ('test-formula.R:11:3'): formula function works correctly ───────────── Error in `expect(is.call(formula(fit)))`: `failure_message` must be a character vector, not absent. Backtrace: ▆ 1. └─testthat::expect(failure_message = ) at test-formula.R:11:3 2. └─testthat:::check_character(failure_message) 3. └─testthat:::stop_input_type(...) 4. └─rlang::abort(message, ..., call = call, arg = arg) ── Error ('test-logLik.R:11:3'): logLik function works correctly ─────────────── Error in `expect(round(logLik(fit), 5), -9.22893)`: `ok` must be `TRUE` or `FALSE`, not a object. Backtrace: ▆ 1. └─testthat::expect(ok = round(logLik(fit), 5)) at test-logLik.R:11:3 2. └─testthat:::check_bool(ok) 3. └─testthat:::stop_input_type(...) 4. └─rlang::abort(message, ..., call = call, arg = arg) ── Error ('test-nobs.R:11:3'): nobs function works correctly ─────────────────── Error in `expect(nobs(fit), 60)`: `ok` must be `TRUE` or `FALSE`, not the number 60. Backtrace: ▆ 1. └─testthat::expect(ok = nobs(fit)) at test-nobs.R:11:3 2. └─testthat:::check_bool(ok) 3. └─testthat:::stop_input_type(...) 4. └─rlang::abort(message, ..., call = call, arg = arg) [ FAIL 3 | WARN 0 | SKIP 0 | PASS 552 ] Error: ! Test failures. Execution halted Package: howzatR Check: tests New result: ERROR Running ‘testthat.R’ [2s/2s] Running the tests in ‘tests/testthat.R’ failed. Complete output: > library(testthat) > library(howzatR) > > test_check("howzatR") Saving _problems/test-batting_basics-7.R Saving _problems/test-batting_basics-22.R [ FAIL 2 | WARN 0 | SKIP 0 | PASS 16 ] ══ Failed tests ════════════════════════════════════════════════════════════════ ── Error ('test-batting_basics.R:7:3'): bat_avg works ────────────────────────── Error in `expect(bat_avg(runs_scored = 50, no_dismissals = 2), 25)`: `ok` must be `TRUE` or `FALSE`, not the number 25. Backtrace: ▆ 1. └─testthat::expect(ok = bat_avg(runs_scored = 50, no_dismissals = 2)) at test-batting_basics.R:7:3 2. └─testthat:::check_bool(ok) 3. └─testthat:::stop_input_type(...) 4. └─rlang::abort(message, ..., call = call, arg = arg) ── Error ('test-batting_basics.R:22:3'): bat_sr works ────────────────────────── Error in `expect(bat_sr(runs_scored = 250, balls_faced = 200), 125)`: `ok` must be `TRUE` or `FALSE`, not the number 125. Backtrace: ▆ 1. └─testthat::expect(ok = bat_sr(runs_scored = 250, balls_faced = 200)) at test-batting_basics.R:22:3 2. └─testthat:::check_bool(ok) 3. └─testthat:::stop_input_type(...) 4. └─rlang::abort(message, ..., call = call, arg = arg) [ FAIL 2 | WARN 0 | SKIP 0 | PASS 16 ] Error: ! Test failures. Execution halted Package: htmltools Check: tests New result: ERROR Running ‘test-all.R’ [28s/29s] Running the tests in ‘tests/test-all.R’ failed. Complete output: > library(testthat) > library(htmltools) > > test_check("htmltools") Saving _problems/test-tag-query-128.R [ FAIL 1 | WARN 0 | SKIP 7 | PASS 10196 ] ══ Skipped tests (7) ═══════════════════════════════════════════════════════════ • Chinese locale not available (2): 'test-tags.r:988:3', 'test-template.R:59:3' • On CRAN (4): 'test-print.R:54:3', 'test-tag-query.R:705:1', 'test-tags.r:25:1', 'test-tags.r:1067:1' • {knitr} is not installed (1): 'test-tags.r:1205:3' ══ Failed tests ════════════════════════════════════════════════════════════════ ── Error ('test-tag-query.R:123:3'): tagQuery()$find() ───────────────────────── Error in `y$parent`: $ operator is invalid for atomic vectors Backtrace: ▆ 1. ├─testthat::expect_failure(...) at test-tag-query.R:123:3 2. │ └─testthat:::capture_success_failure(expr) 3. │ └─base::withCallingHandlers(...) 4. └─htmltools:::expect_equal_tags(x$selectedTags(), newX$selectedTags()) 5. └─htmltools (local) expect_equal_tags_(x, y) at ./helper-tags.R:25:3 6. └─base::Map(x, y, f = expect_equal_tags_) at ./helper-tags.R:16:7 7. └─base::mapply(FUN = f, ..., SIMPLIFY = FALSE) 8. └─htmltools (local) ``(dots[[1L]][[1L]], dots[[2L]][[1L]]) 9. └─htmltools (local) expect_equal_tags_(x$children, y$children) at ./helper-tags.R:12:7 10. └─base::Map(x, y, f = expect_equal_tags_) at ./helper-tags.R:16:7 11. └─base::mapply(FUN = f, ..., SIMPLIFY = FALSE) 12. └─htmltools (local) ``(dots[[1L]][[1L]], dots[[2L]][[1L]]) 13. └─testthat::expect_equal(y$parent, NULL) at ./helper-tags.R:8:7 14. └─testthat::quasi_label(enquo(object), label) 15. └─rlang::eval_bare(expr, quo_get_env(quo)) [ FAIL 1 | WARN 0 | SKIP 7 | PASS 10196 ] Error: ! Test failures. Execution halted Package: httptest Check: tests New result: ERROR Running ‘spelling.R’ [0s/0s] Running ‘testthat.R’ [12s/14s] Running the tests in ‘tests/testthat.R’ failed. Complete output: > library(testthat) > test_check("httptest") Loading required package: httptest Saving _problems/test-expect-header-18.R Saving _problems/test-expect-header-33.R [ FAIL 2 | WARN 0 | SKIP 12 | PASS 289 ] ══ Skipped tests (12) ══════════════════════════════════════════════════════════ • On CRAN (8): 'test-capture-requests.R:6:3', 'test-capture-requests.R:50:3', 'test-capture-requests.R:95:3', 'test-expect-header.R:108:5', 'test-mock-dir.R:22:5', 'test-offline.R:12:7', 'test-offline.R:19:9', 'test-without-internet.R:3:5' • TODO: handle URL escaping (1): 'test-redact.R:243:5' • TODO: prettify when simplify=FALSE (1): 'test-capture-requests.R:175:5' • third_edition is TRUE (2): 'test-expect-header.R:61:7', 'test-without-internet.R:73:7' ══ Failed tests ════════════════════════════════════════════════════════════════ ── Failure ('test-expect-header.R:10:7'): expect_header with fake HTTP ───────── Expected exactly one failure and no successes. Actually succeeded 1 times Backtrace: ▆ 1. ├─httptest::expect_GET(...) at test-expect-header.R:10:7 2. │ └─httptest:::expect_mock_request(object, "GET ", url, " ", ...) 3. │ └─request_happened()(...) 4. │ └─testthat:::expect_condition_matching_(...) 5. │ └─testthat:::quasi_capture(...) 6. │ ├─testthat (local) .capture(...) 7. │ │ └─base::withCallingHandlers(...) 8. │ └─rlang::eval_bare(quo_get_expr(.quo), quo_get_env(.quo)) 9. └─testthat::expect_failure(...) ── Failure ('test-expect-header.R:25:7'): expect_header with fake HTTP ───────── Expected exactly one failure and no successes. Actually succeeded 1 times Backtrace: ▆ 1. ├─httptest::expect_POST(...) at test-expect-header.R:25:7 2. │ └─httptest:::expect_mock_request(object, "POST ", url, " ", ...) 3. │ └─request_happened()(...) 4. │ └─testthat:::expect_condition_matching_(...) 5. │ └─testthat:::quasi_capture(...) 6. │ ├─testthat (local) .capture(...) 7. │ │ └─base::withCallingHandlers(...) 8. │ └─rlang::eval_bare(quo_get_expr(.quo), quo_get_env(.quo)) 9. └─testthat::expect_failure(...) [ FAIL 2 | WARN 0 | SKIP 12 | PASS 289 ] Error: ! Test failures. Execution halted Package: httptest2 Check: tests New result: ERROR Running ‘spelling.R’ [0s/0s] Running ‘testthat.R’ [9s/9s] Running the tests in ‘tests/testthat.R’ failed. Complete output: > library(testthat) > test_check("httptest2") Loading required package: httptest2 Saving _problems/test-expect-request-header-31.R Saving _problems/test-expect-request-header-40.R Saving _problems/test-expect-request-header-162.R Saving _problems/test-expect-request-46.R Saving _problems/test-expect-request-88.R [ FAIL 5 | WARN 0 | SKIP 2 | PASS 233 ] ══ Skipped tests (2) ═══════════════════════════════════════════════════════════ • TODO: handle URL escaping (1): 'test-redact.R:180:5' • TODO: prettify when simplify=FALSE (1): 'test-capture-requests.R:227:5' ══ Failed tests ════════════════════════════════════════════════════════════════ ── Failure ('test-expect-request-header.R:23:5'): expect_request_header with mock API ── Expected failure message to match regexp "Header \"accept\" does not match \"image/jpeg\"". Actual message: x | Expected Header "accept" to match regexp "image/jpeg". | Actual text: | x | image/png ── Failure ('test-expect-request-header.R:32:5'): expect_request_header with mock API ── Expected failure message to match regexp "Header \"accept\" is not NULL". Actual message: x | Expected Header "accept" to be NULL. | Differences: | `actual` is a character vector ('image/png') | `expected` is NULL | ── Failure ('test-expect-request-header.R:153:5'): expect_request_header without_internet ── Expected `object` not to throw any errors. Actually got a with message: An unexpected request was made: GET http://httpbin.not/ Backtrace: ▆ 1. ├─httptest2::expect_no_request(...) at test-expect-request-header.R:153:5 2. │ └─testthat::expect_error(object, NA, ..., class = "httptest2_request") 3. │ └─testthat:::expect_condition_matching_(...) 4. │ └─testthat:::quasi_capture(...) 5. │ ├─testthat (local) .capture(...) 6. │ │ └─base::withCallingHandlers(...) 7. │ └─rlang::eval_bare(quo_get_expr(.quo), quo_get_env(.quo)) 8. ├─testthat::expect_failure(...) 9. │ └─testthat:::capture_success_failure(expr) 10. │ └─base::withCallingHandlers(...) 11. ├─httptest2::expect_request_header(...) 12. │ └─httptest2:::with_mocked_responses(header_mocker, expr) 13. │ └─(utils::getFromNamespace("with_mocked_responses", "httr2"))(...) 14. │ └─withr::with_options(list(httr2_mock = mock), code) 15. │ └─base::force(code) 16. ├─... %>% req_perform() 17. └─httr2::req_perform(.) 18. └─httptest2 (local) mock(req) 19. └─httptest2 (local) current_mocker(req) 20. └─rlang::abort(out, mockfile = req$mockfile, class = "httptest2_request") ── Failure ('test-expect-request.R:43:7'): expect_VERB when no request is made ── Expected failure message to match regexp "No request was made". Actual message: x | Expected `object` to throw a error with class . Backtrace: ▆ 1. ├─base::suppressWarnings(...) at test-expect-request.R:41:5 2. │ └─base::withCallingHandlers(...) 3. └─testthat::expect_failure(expect_POST("just a string"), "No request was made") at test-expect-request.R:43:7 ── Error ('test-expect-request.R:75:5'): expect_request without_internet ─────── Error in `mock(req)`: An unexpected request was made: POST http://httpbin.not/get {"test":true} Backtrace: ▆ 1. ├─testthat::expect_failure(...) at test-expect-request.R:75:5 2. │ └─testthat:::capture_success_failure(expr) 3. │ └─base::withCallingHandlers(...) 4. ├─httptest2::expect_POST(...) 5. │ └─httptest2:::expect_request(object, "POST ", url, " ", ...) 6. │ ├─base::withCallingHandlers(...) 7. │ └─testthat::expect_error(...) 8. │ └─testthat:::expect_condition_matching_(...) 9. │ └─testthat:::quasi_capture(...) 10. │ ├─testthat (local) .capture(...) 11. │ │ └─base::withCallingHandlers(...) 12. │ └─rlang::eval_bare(quo_get_expr(.quo), quo_get_env(.quo)) 13. └─httr2::req_perform(this_req) 14. └─httptest2 (local) mock(req) 15. └─rlang::abort(out, mockfile = req$mockfile, class = "httptest2_request") [ FAIL 5 | WARN 0 | SKIP 2 | PASS 233 ] Error: ! Test failures. Execution halted Package: humanize Check: tests New result: ERROR Running ‘testthat.R’ [2s/2s] Running the tests in ‘tests/testthat.R’ failed. Complete output: > library(testthat) > library(humanize) > > test_check("humanize") Saving _problems/test_time-150.R Saving _problems/test_time-219.R [ FAIL 2 | WARN 0 | SKIP 0 | PASS 96 ] ══ Failed tests ════════════════════════════════════════════════════════════════ ── Error ('test_time.R:145:3'): natural_time works as expected ───────────────── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test_time.R:145:3 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) ── Error ('test_time.R:214:3'): natural_time no months works as expected ─────── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test_time.R:214:3 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) [ FAIL 2 | WARN 0 | SKIP 0 | PASS 96 ] Error: ! Test failures. Execution halted Package: HurreconR Check: tests New result: ERROR Running ‘testthat.R’ [1s/1s] Running the tests in ‘tests/testthat.R’ failed. Complete output: > # This file is part of the standard setup for testthat. > # It is recommended that you do not modify it. > # > # Where should you do additional test configuration? > # Learn more about the roles of various files in: > # * https://r-pkgs.org/tests.html > # * https://testthat.r-lib.org/reference/test_package.html#special-files > > library(testthat) > library(HurreconR) > > test_check("HurreconR") Path set to /home/hornik/tmp/CRAN_recheck/HurreconR.Rcheck/HurreconR/ ... Modeling site ... 028 ms Saving _problems/test_HurreconR-15.R [ FAIL 1 | WARN 1 | SKIP 0 | PASS 0 ] ══ Failed tests ════════════════════════════════════════════════════════════════ ── Error ('test_HurreconR.R:14:1'): (code run outside of `test_that()`) ──────── Error in `expect_snapshot_value(hurrecon_summarize_site(hur_id = "AL1935-03", site_name = "Miami FL", hur_path = hur_path), test.expected, style = "serialize", cran = FALSE)`: `tolerance` must be a number, not the string "/home/hornik/tmp/CRAN_reche...". Backtrace: ▆ 1. └─testthat::expect_snapshot_value(tolerance = test.expected) at test_HurreconR.R:14:1 2. └─testthat:::check_number_decimal(tolerance, min = 0) 3. └─testthat:::.stop_not_number(...) 4. └─testthat:::stop_input_type(...) 5. └─rlang::abort(message, ..., call = call, arg = arg) [ FAIL 1 | WARN 1 | SKIP 0 | PASS 0 ] Error: ! Test failures. Execution halted Package: hySpc.testthat Check: tests New result: ERROR Running ‘testthat.R’ [1s/1s] Running the tests in ‘tests/testthat.R’ failed. Complete output: > library(testthat) > library(hySpc.testthat) > > test_check("hySpc.testthat") Saving _problems/test_attached-3.R Saving _problems/test_attached-3.R [ FAIL 2 | WARN 0 | SKIP 0 | PASS 2 ] ══ Failed tests ════════════════════════════════════════════════════════════════ ── Error ('test_attached.R:3:1'): correct execution ──────────────────────────── Error in `identical(self$running[[self$current_file]]$context, context)`: attempt to use zero-length variable name Backtrace: ▆ 1. └─hySpc.testthat::test_fun(f, reporter = "list") 2. ├─testthat::with_reporter(reporter = reporter, test()) 3. │ └─base::tryCatch(...) 4. │ └─base (local) tryCatchList(expr, classes, parentenv, handlers) 5. │ └─base (local) tryCatchOne(expr, names, parentenv, handlers[[1L]]) 6. │ └─base (local) doTryCatch(return(expr), name, parentenv, handler) 7. └─hySpc.testthat (local) test() 8. └─testthat::test_that(...) 9. └─testthat:::test_code(code, parent.frame()) 10. └─reporter$start_test(context = reporter$.context, test = test) 11. └─base::identical(self$running[[self$current_file]]$context, context) ── Error ('test_attached.R:3:1'): standalone ─────────────────────────────────── Error in `identical(self$running[[self$current_file]]$context, context)`: attempt to use zero-length variable name Backtrace: ▆ 1. └─hySpc.testthat::unittest(ns = test_env, reporter = "list") 2. ├─testthat::with_reporter(...) 3. │ └─base::tryCatch(...) 4. │ └─base (local) tryCatchList(expr, classes, parentenv, handlers) 5. │ └─base (local) tryCatchOne(expr, names, parentenv, handlers[[1L]]) 6. │ └─base (local) doTryCatch(return(expr), name, parentenv, handler) 7. └─hySpc.testthat (local) t() 8. └─testthat::test_that(...) 9. └─testthat:::test_code(code, parent.frame()) 10. └─reporter$start_test(context = reporter$.context, test = test) 11. └─base::identical(self$running[[self$current_file]]$context, context) [ FAIL 2 | WARN 0 | SKIP 0 | PASS 2 ] Error: ! Test failures. Execution halted Package: idiolect Check: tests New result: ERROR Running ‘testthat.R’ [169s/168s] Running the tests in ‘tests/testthat.R’ failed. Complete output: > # This file is part of the standard setup for testthat. > # It is recommended that you do not modify it. > # > # Where should you do additional test configuration? > # Learn more about the roles of various files in: > # * https://r-pkgs.org/tests.html > # * https://testthat.r-lib.org/reference/test_package.html#special-files > > library(testthat) > library(idiolect) Loading required package: quanteda Package version: 4.3.1 Unicode version: 16.0 ICU version: 76.1 Parallel computing: 3 of 32 threads used. See https://quanteda.io for tutorials and examples. > > test_check("idiolect") Saving _problems/test-impostors-35.R Saving _problems/test-impostors-84.R Saving _problems/test-impostors-123.R | | | 0% | |= | 1% | |= | 2% | |== | 3% | |=== | 4% | |==== | 5% | |==== | 6% | |===== | 7% | |====== | 8% | |======= | 9% | |======= | 11% | |======== | 12% | |========= | 13% | |========== | 14% | |========== | 15% | |=========== | 16% | |============ | 17% | |============= | 18% | |============= | 19% | |============== | 20% | |=============== | 21% | |=============== | 22% | |================ | 23% | |================= | 24% | |================== | 25% | |================== | 26% | |=================== | 27% | |==================== | 28% | |===================== | 29% | |===================== | 31% | |====================== | 32% | |======================= | 33% | |======================== | 34% | |======================== | 35% | |========================= | 36% | |========================== | 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|================================================================== | 94% | |================================================================== | 95% | |=================================================================== | 96% | |==================================================================== | 97% | |===================================================================== | 98% | |===================================================================== | 99% | |======================================================================| 100% Setting levels: control = FALSE, case = TRUE Setting direction: controls < cases Setting levels: control = FALSE, case = TRUE Setting direction: controls < cases [ FAIL 3 | WARN 1 | SKIP 11 | PASS 14 ] ══ Skipped tests (11) ══════════════════════════════════════════════════════════ • On CRAN (9): 'test-calibrate_LLR.R:1:1', 'test-chunk_texts.R:1:1', 'test-concordance.R:1:1', 'test-create_corpus.R:1:1', 'test-delta.R:1:1', 'test-lambdaG_visualize.R:1:1', 'test-most_similar.R:1:1', 'test-ngram_tracing.R:1:1', 'test-vectorize.R:1:1' • spacyr environment not present (2): 'test-contentmask.R:4:3', 'test-tokenize_sents.R:4:3' ══ Failed tests ════════════════════════════════════════════════════════════════ ── Error ('test-impostors.R:35:3'): RBI works ────────────────────────────────── Error in `testthat::expect(results.corpus[1, 4], 0.54)`: `ok` must be `TRUE` or `FALSE`, not the number 0.54. Backtrace: ▆ 1. └─testthat::expect(ok = results.corpus[1, 4]) at test-impostors.R:35:3 2. └─testthat:::check_bool(ok) 3. └─testthat:::stop_input_type(...) 4. └─rlang::abort(message, ..., call = call, arg = arg) ── Error ('test-impostors.R:84:3'): KGI works ────────────────────────────────── Error in `testthat::expect(results.corpus[1, 4], 0.75)`: `ok` must be `TRUE` or `FALSE`, not the number 0.75. Backtrace: ▆ 1. └─testthat::expect(ok = results.corpus[1, 4]) at test-impostors.R:84:3 2. └─testthat:::check_bool(ok) 3. └─testthat:::stop_input_type(...) 4. └─rlang::abort(message, ..., call = call, arg = arg) ── Error ('test-impostors.R:123:3'): IM works ────────────────────────────────── Error in `testthat::expect(results.corpus[4, 4], 0.57)`: `ok` must be `TRUE` or `FALSE`, not the number 0.57. Backtrace: ▆ 1. └─testthat::expect(ok = results.corpus[4, 4]) at test-impostors.R:123:3 2. └─testthat:::check_bool(ok) 3. └─testthat:::stop_input_type(...) 4. └─rlang::abort(message, ..., call = call, arg = arg) [ FAIL 3 | WARN 1 | SKIP 11 | PASS 14 ] Error: ! Test failures. Execution halted Package: imager Check: tests New result: ERROR Running ‘testthat.R’ [35s/36s] Running the tests in ‘tests/testthat.R’ failed. Complete output: > library(testthat) > library(imager) Loading required package: magrittr Attaching package: 'magrittr' The following objects are masked from 'package:testthat': equals, is_less_than, not Attaching package: 'imager' The following object is masked from 'package:magrittr': add The following objects are masked from 'package:stats': convolve, spectrum The following object is masked from 'package:graphics': frame The following object is masked from 'package:base': save.image > > test_check("imager") Saving _problems/test_load_save_videos-15.R [ FAIL 1 | WARN 0 | SKIP 0 | PASS 19 ] ══ Failed tests ════════════════════════════════════════════════════════════════ ── Error ('test_load_save_videos.R:15:13'): load_and_save_videos ─────────────── Error in `expect(file_test("-f", ff), TRUE)`: `failure_message` must be a character vector, not `TRUE`. Backtrace: ▆ 1. └─testthat::expect(failure_message = TRUE) at test_load_save_videos.R:15:13 2. └─testthat:::check_character(failure_message) 3. └─testthat:::stop_input_type(...) 4. └─rlang::abort(message, ..., call = call, arg = arg) [ FAIL 1 | WARN 0 | SKIP 0 | PASS 19 ] Error: ! Test failures. Execution halted Package: IMEC Check: tests New result: ERROR Running ‘testthat.R’ [20s/20s] Running the tests in ‘tests/testthat.R’ failed. Complete output: > library(testthat) > library(IMEC) > > test_check("IMEC") [1] "The used edge weight is 0.333333333333333" [1] "The used edge weight is 0.5" [1] "The used edge weight is 0.333333333333333" [1] "The used edge weight is 0.5" [1] "The used edge weight is 0.333333333333333" [1] "The used edge weight is 0.5" [1] "The used edge weight is 0.5" [1] "The used edge weight is 0.333333333333333" [1] "The used edge weight is 0.333333333333333" [1] "The used edge weight is 0.5" [1] "The used edge weight is -4" [1] "The used edge weight is -4" Press [enter] to continue Saving _problems/test-0-basictests-29.R [1] "The used edge weight is 0.333333333333333" [1] "The used edge weight is 0.5" [1] "The used edge weight is 0.333333333333333" [1] "The used edge weight is 0.5" [1] "The used edge weight is 0.333333333333333" [1] "The used edge weight is 0.5" [1] "The used edge weight is 0.5" [1] "The used edge weight is -4" [1] "The used edge weight is -4" Press [enter] to continue Saving _problems/test-0-basictests-60.R [1] "The used edge weight is 0.333333333333333" [1] "The used edge weight is 0.5" [1] "The used edge weight is 0.333333333333333" [1] "The used edge weight is 0.5" [1] "The used edge weight is 0.333333333333333" [1] "The used edge weight is 0.5" [1] "The used edge weight is 0.5" [1] "The used edge weight is 0.333333333333333" [1] "The used edge weight is 0.333333333333333" [1] "The used edge weight is 0.5" [1] "The used edge weight is -4" [1] "The used edge weight is -4" [ FAIL 2 | WARN 2 | SKIP 1 | PASS 2 ] ══ Skipped tests (1) ═══════════════════════════════════════════════════════════ • On CRAN (1): 'test-0-basictests.R:105:1' ══ Failed tests ════════════════════════════════════════════════════════════════ ── Error ('test-0-basictests.R:29:3'): analytic way of calculating coherence works ── Error in `expect(0 < mean(IMEC$ExplanatoryCoherenceT1[[2]]))`: `failure_message` must be a character vector, not absent. Backtrace: ▆ 1. └─testthat::expect(failure_message = ) at test-0-basictests.R:29:3 2. └─testthat:::check_character(failure_message) 3. └─testthat:::stop_input_type(...) 4. └─rlang::abort(message, ..., call = call, arg = arg) ── Error ('test-0-basictests.R:60:3'): analytic way of calculating coherence works for 1 theory ── Error in `expect(0 < mean(IMEC$ExplanatoryCoherenceT1[[2]]))`: `failure_message` must be a character vector, not absent. Backtrace: ▆ 1. └─testthat::expect(failure_message = ) at test-0-basictests.R:60:3 2. └─testthat:::check_character(failure_message) 3. └─testthat:::stop_input_type(...) 4. └─rlang::abort(message, ..., call = call, arg = arg) [ FAIL 2 | WARN 2 | SKIP 1 | PASS 2 ] Error: ! Test failures. Execution halted Package: iRfcb Check: re-building of vignette outputs New result: ERROR Error(s) in re-building vignettes: ... --- re-building ‘a-general-tutorial.Rmd’ using rmarkdown Quitting from a-general-tutorial.Rmd:41-49 [unnamed-chunk-4] ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Error in `ifcb_download_test_data()`: ! Download failed after 10 attempts. --- Backtrace: ▆ 1. └─iRfcb::ifcb_download_test_data(...) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Error: processing vignette 'a-general-tutorial.Rmd' failed with diagnostics: Download failed after 10 attempts. --- failed re-building ‘a-general-tutorial.Rmd’ --- re-building ‘qc-tutorial.Rmd’ using rmarkdown The virtual environment was not created successfully because ensurepip is not available. On Debian/Ubuntu systems, you need to install the python3-venv package using the following command. apt install python3.13-venv You may need to use sudo with that command. After installing the python3-venv package, recreate your virtual environment. Failing command: /home/hornik/tmp/scratch/Rtmpnr0oAr/iRfcb/bin/python3.13 /home/hornik/tmp/scratch/check-CRAN-incoming-hornik/cache/R/reticulate/uv/cache/archive-v0/qoVXOr13fuf7jDQ3r10pj/bin/python: No module named pip [WARNING] Deprecated: --highlight-style. Use --syntax-highlighting instead. --- finished re-building ‘qc-tutorial.Rmd’ SUMMARY: processing the following file failed: ‘a-general-tutorial.Rmd’ Error: Vignette re-building failed. Execution halted Package: learnr Check: tests New result: ERROR Running ‘testthat.R’ [33s/34s] Running the tests in ‘tests/testthat.R’ failed. Complete output: > > if (requireNamespace("testthat")) { + library(testthat) + library(learnr) + + test_check("learnr") + } Loading required namespace: testthat Saving _problems/test-exercise-251.R [ FAIL 1 | WARN 0 | SKIP 20 | PASS 812 ] ══ Skipped tests (20) ══════════════════════════════════════════════════════════ • On CRAN (18): 'test-available-tutorials.R:4:1', 'test-evaluators.R:2:3', 'test-evaluators.R:92:3', 'test-evaluators.R:137:3', 'test-evaluators.R:179:3', 'test-evaluators.R:212:3', 'test-evaluators.R:244:3', 'test-evaluators.R:279:3', 'test-evaluators.R:363:3', 'test-exercise.R:356:1', 'test-exercise.R:1237:3', 'test-exercise.R:1345:1', 'test-exercise.R:1393:1', 'test-exercise.R:1444:3', 'test-exercise.R:1472:3', 'test-exercise.R:1524:1', 'test-shinytest2-aaa.R:2:1', 'test-shinytest2-hints.R:2:1' • Skipping test because LANG is C.UTF-8 (2): 'test-i18n.R:208:3', 'test-i18n.R:238:3' ══ Failed tests ════════════════════════════════════════════════════════════════ ── Error ('test-exercise.R:246:3'): evaluate_exercise() returns an internal error when `render_exercise()` fails ── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test-exercise.R:246:3 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) [ FAIL 1 | WARN 0 | SKIP 20 | PASS 812 ] Error: ! Test failures. Execution halted Package: lintr Check: tests New result: ERROR Running ‘testthat.R’ [109s/112s] Running the tests in ‘tests/testthat.R’ failed. Complete output: > library(testthat) > library(lintr) > > # suppress printing environment name (noisy) > invisible({ + loadNamespace("patrick") + loadNamespace("withr") + }) > > test_check("lintr") Saving _problems/test-expect_lint-23.R Saving _problems/test-expect_lint-24.R Saving _problems/test-expect_lint-30.R Saving _problems/test-expect_lint-39.R Saving _problems/test-expect_lint-41.R Saving _problems/test-expect_lint-42.R Saving _problems/test-expect_lint-43.R Saving _problems/test-expect_lint-44.R Saving _problems/test-expect_lint-45.R Saving _problems/test-expect_lint-46.R Saving _problems/test-expect_lint-48.R Saving _problems/test-expect_lint-49.R Saving _problems/test-expect_lint-52.R i No lints found. i No lints found. [ FAIL 13 | WARN 0 | SKIP 5 | PASS 6551 ] ══ Skipped tests (5) ═══════════════════════════════════════════════════════════ • On CRAN (4): 'test-methods.R:184:3', 'test-methods.R:184:3', 'test-methods.R:184:3', 'test-methods.R:184:3' • {data.table} is not installed (1): 'test-methods.R:163:3' ══ Failed tests ════════════════════════════════════════════════════════════════ ── Failure ('test-expect_lint.R:23:3'): single check ─────────────────────────── Expected exactly one success and no failures. Actually failed 1 times ── Failure ('test-expect_lint.R:24:3'): single check ─────────────────────────── Expected exactly one failure and no successes. Actually succeeded 1 times ── Failure ('test-expect_lint.R:30:3'): single check ─────────────────────────── Expected exactly one success and no failures. Actually succeeded 2 times ── Failure ('test-expect_lint.R:37:3'): multiple checks ──────────────────────── Expected exactly one success and no failures. Actually succeeded 9 times ── Failure ('test-expect_lint.R:41:3'): multiple checks ──────────────────────── Expected exactly one success and no failures. Actually succeeded 2 times ── Failure ('test-expect_lint.R:42:3'): multiple checks ──────────────────────── Expected exactly one success and no failures. Actually succeeded 2 times ── Failure ('test-expect_lint.R:43:3'): multiple checks ──────────────────────── Expected exactly one success and no failures. Actually succeeded 2 times ── Failure ('test-expect_lint.R:44:3'): multiple checks ──────────────────────── Expected exactly one success and no failures. Actually succeeded 2 times ── Failure ('test-expect_lint.R:45:3'): multiple checks ──────────────────────── Expected exactly one success and no failures. Actually succeeded 2 times ── Failure ('test-expect_lint.R:46:3'): multiple checks ──────────────────────── Expected exactly one failure and no successes. Actually succeeded 1 times ── Failure ('test-expect_lint.R:48:3'): multiple checks ──────────────────────── Expected exactly one success and no failures. Actually failed 1 times ── Failure ('test-expect_lint.R:49:3'): multiple checks ──────────────────────── Expected exactly one failure and no successes. Actually failed 2 times ── Failure ('test-expect_lint.R:50:3'): multiple checks ──────────────────────── Expected exactly one success and no failures. Actually succeeded 3 times [ FAIL 13 | WARN 0 | SKIP 5 | PASS 6551 ] Error: ! Test failures. Execution halted Package: malariaAtlas Check: re-building of vignette outputs New result: ERROR Error(s) in re-building vignettes: ... --- re-building ‘overview.Rmd’ using rmarkdown Quitting from overview.Rmd:199-201 [unnamed-chunk-25] ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Error: ! Cannot open "/home/hornik/tmp/scratch/RtmpJdQLhu/file21515a417d37eb.json"; The source could be corrupt or not supported. See `st_drivers()` for a list of supported formats. --- Backtrace: ▆ 1. ├─ggplot2::autoplot(MDG_pr_data) 2. └─malariaAtlas:::autoplot.pr.points(MDG_pr_data) 3. └─malariaAtlas::getShp(ISO = unique_iso, format = "df", admin_level = admin_level_request) 4. └─malariaAtlas::listShp(...) 5. └─future.apply::future_lapply(...) 6. └─future.apply:::future_xapply(...) 7. └─base::tryCatch(...) 8. └─base (local) tryCatchList(expr, classes, parentenv, handlers) 9. └─base (local) tryCatchOne(...) 10. └─value[[3L]](cond) 11. └─future.apply:::onError(e, futures = fs, debug = debug) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Error: processing vignette 'overview.Rmd' failed with diagnostics: Cannot open "/home/hornik/tmp/scratch/RtmpJdQLhu/file21515a417d37eb.json"; The source could be corrupt or not supported. See `st_drivers()` for a list of supported formats. --- failed re-building ‘overview.Rmd’ SUMMARY: processing the following file failed: ‘overview.Rmd’ Error: Vignette re-building failed. Execution halted Package: manipulateWidget Check: tests New result: ERROR Running ‘testthat.R’ [7s/7s] Running the tests in ‘tests/testthat.R’ failed. Complete output: > library(testthat) > library(manipulateWidget) > > test_check("manipulateWidget") Saving _problems/test-on_done-20.R Saving _problems/test-on_done-42.R [ FAIL 2 | WARN 0 | SKIP 1 | PASS 650 ] ══ Skipped tests (1) ═══════════════════════════════════════════════════════════ • empty test (1): 'test-inputs.R:97:1' ══ Failed tests ════════════════════════════════════════════════════════════════ ── Error ('test-on_done.R:5:5'): onDone / stops the shiny gadget and returns a htmlwidget ── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test-on_done.R:5:5 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) ── Error ('test-on_done.R:24:23'): onDone / returns a combined widget if comparison ── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. ├─base::suppressWarnings(...) at test-on_done.R:24:5 2. │ └─base::withCallingHandlers(...) 3. └─testthat::with_mock(...) at test-on_done.R:24:23 4. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 5. └─lifecycle:::deprecate_stop0(msg) 6. └─rlang::cnd_signal(...) [ FAIL 2 | WARN 0 | SKIP 1 | PASS 650 ] Error: ! Test failures. Execution halted Package: markmyassignment Check: tests New result: ERROR Running ‘testthat.R’ [14s/15s] Running the tests in ‘tests/testthat.R’ failed. Complete output: > library(testthat) > test_check("markmyassignment") Loading required package: markmyassignment Saving _problems/test-expectation-40.R Saving _problems/test-expectation-45.R [ FAIL 2 | WARN 0 | SKIP 6 | PASS 145 ] ══ Skipped tests (6) ═══════════════════════════════════════════════════════════ • On CRAN (6): 'test-set_assignment.R:5:3', 'test-set_assignment.R:47:3', 'test-set_assignment.R:70:3', 'test-set_assignment.R:101:3', 'test-set_assignment.R:155:3', 'test-set_assignment.R:205:3' ══ Failed tests ════════════════════════════════════════════════════════════════ ── Failure ('test-expectation.R:40:3'): expect_function_code() ───────────────── Expected failure message to match regexp "'markmyassignment' not found in the body of base::mean". Actual message: ✖ │ 'markmyassignment' not found in the body of `base::mean` ── Failure ('test-expectation.R:45:3'): expect_no_forbidden_function_code() ──── Expected failure message to match regexp "Forbidden code 'UseMethod' is found in the body of base::mean". Actual message: ✖ │ Forbidden code 'UseMethod' is found in the body of `base::mean` [ FAIL 2 | WARN 0 | SKIP 6 | PASS 145 ] Error: ! Test failures. Execution halted Package: mbbe Check: tests New result: ERROR Running ‘testthat.R’ [30s/30s] Running the tests in ‘tests/testthat.R’ failed. Complete output: > # This file is part of the standard setup for testthat. > # It is recommended that you do not modify it. > # > # Where should you do additional test configuration? > # Learn more about the roles of various files in: > # * https://r-pkgs.org/testing-design.html#sec-tests-files-overview > # * https://testthat.r-lib.org/articles/special-files.html > > library(testthat) > library(mbbe) > > test_check("mbbe") Saving _problems/test-check_requirements-52.R [ FAIL 1 | WARN 0 | SKIP 0 | PASS 11 ] ══ Failed tests ════════════════════════════════════════════════════════════════ ── Error ('test-check_requirements.R:39:3'): check_requirements works ────────── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test-check_requirements.R:39:3 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) [ FAIL 1 | WARN 0 | SKIP 0 | PASS 11 ] Error: ! Test failures. Execution halted Package: MetaComp Check: tests New result: ERROR Running ‘testthat.R’ [18s/18s] Running the tests in ‘tests/testthat.R’ failed. Complete output: > library(testthat) > library(MetaComp) > > test_check("MetaComp") [1] "gottcha" [1] "kraken" Saving _problems/test_load_metaphlan_assignment-14.R Saving _problems/test_pipeline-37.R Saving _problems/test_plot_bwa_assignment-31.R Saving _problems/test_plot_diamond_assignment-31.R Saving _problems/test_plot_gottcha_assignment-31.R Saving _problems/test_plot_kraken_assignment-31.R Saving _problems/test_plot_merged_assignment_b-80.R Saving _problems/test_plot_merged_assignment_d-80.R Saving _problems/test_plot_merged_assignment_g-78.R Saving _problems/test_plot_merged_assignment_g2-78.R Saving _problems/test_plot_merged_assignment_k-80.R Saving _problems/test_plot_merged_assignment_m-80.R Saving _problems/test_plot_merged_assignment_p-81.R Saving _problems/test_plot_metaphlan_assignment-31.R Saving _problems/test_plot_pangia_assignment-31.R [ FAIL 15 | WARN 21 | SKIP 0 | PASS 108 ] ══ Failed tests ════════════════════════════════════════════════════════════════ ── Error ('test_load_metaphlan_assignment.R:14:1'): (code run outside of `test_that()`) ── Error in `matches("LEVEL")`: could not find function "matches" Backtrace: ▆ 1. └─testthat::expect_that(colnames(dat)[1], matches("LEVEL")) at test_load_metaphlan_assignment.R:14:1 ── Error ('test_pipeline.R:37:1'): (code run outside of `test_that()`) ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(...) at test_pipeline.R:37:1 ── Error ('test_plot_bwa_assignment.R:31:1'): (code run outside of `test_that()`) ── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(file.exists(png_name), is_true()) at test_plot_bwa_assignment.R:31:1 ── Error ('test_plot_diamond_assignment.R:31:1'): (code run outside of `test_that()`) ── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(file.exists(png_name), is_true()) at test_plot_diamond_assignment.R:31:1 ── Error ('test_plot_gottcha_assignment.R:31:1'): (code run outside of `test_that()`) ── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(file.exists(png_name), is_true()) at test_plot_gottcha_assignment.R:31:1 ── Error ('test_plot_kraken_assignment.R:31:1'): (code run outside of `test_that()`) ── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(file.exists(png_name), is_true()) at test_plot_kraken_assignment.R:31:1 ── Error ('test_plot_merged_assignment_b.R:80:1'): (code run outside of `test_that()`) ── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(...) at test_plot_merged_assignment_b.R:80:1 ── Error ('test_plot_merged_assignment_d.R:80:1'): (code run outside of `test_that()`) ── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(...) at test_plot_merged_assignment_d.R:80:1 ── Error ('test_plot_merged_assignment_g.R:78:1'): (code run outside of `test_that()`) ── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(...) at test_plot_merged_assignment_g.R:78:1 ── Error ('test_plot_merged_assignment_g2.R:78:1'): (code run outside of `test_that()`) ── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(...) at test_plot_merged_assignment_g2.R:78:1 ── Error ('test_plot_merged_assignment_k.R:80:1'): (code run outside of `test_that()`) ── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(...) at test_plot_merged_assignment_k.R:80:1 ── Error ('test_plot_merged_assignment_m.R:80:1'): (code run outside of `test_that()`) ── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(...) at test_plot_merged_assignment_m.R:80:1 ── Error ('test_plot_merged_assignment_p.R:81:1'): (code run outside of `test_that()`) ── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(...) at test_plot_merged_assignment_p.R:81:1 ── Error ('test_plot_metaphlan_assignment.R:31:1'): (code run outside of `test_that()`) ── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(file.exists(png_name), is_true()) at test_plot_metaphlan_assignment.R:31:1 ── Error ('test_plot_pangia_assignment.R:31:1'): (code run outside of `test_that()`) ── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(file.exists(png_name), is_true()) at test_plot_pangia_assignment.R:31:1 [ FAIL 15 | WARN 21 | SKIP 0 | PASS 108 ] Error: ! Test failures. Execution halted Package: metaDigitise Check: tests New result: ERROR Running ‘testthat.R’ [3s/3s] Running the tests in ‘tests/testthat.R’ failed. Complete output: > > library(testthat) > library(metaDigitise) > library(mockery) > > testthat::test_check("metaDigitise") Saving _problems/test-calibrate_coords-13.R Saving _problems/test-calibrate_coords-36.R Saving _problems/test-calibrate_coords-63.R Saving _problems/test-graph_rotation-16.R Saving _problems/test-import_metaDigitise-112.R Saving _problems/test-metaDigitise-24.R Saving _problems/test-misc_func-45.R Saving _problems/test-misc_func-58.R Saving _problems/test-misc_func-74.R Saving _problems/test-misc_func-89.R Saving _problems/test-misc_func-95.R Saving _problems/test-misc_func-115.R Saving _problems/test-misc_func-140.R Saving _problems/test-point_extraction-14.R [ FAIL 14 | WARN 0 | SKIP 0 | PASS 35 ] ══ Failed tests ════════════════════════════════════════════════════════════════ ── Error ('test-calibrate_coords.R:6:2'): Checking cal_coords.. ──────────────── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test-calibrate_coords.R:6:9 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) ── Error ('test-calibrate_coords.R:29:2'): Checking getVals.. ────────────────── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test-calibrate_coords.R:29:9 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) ── Error ('test-calibrate_coords.R:51:2'): Checking user_calibrate.. ─────────── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test-calibrate_coords.R:51:9 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) ── Error ('test-graph_rotation.R:6:2'): Checking user_rotate_graph.. ─────────── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test-graph_rotation.R:6:9 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) ── Error ('test-import_metaDigitise.R:107:2'): Checking import_menu works as expected.. ── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test-import_metaDigitise.R:107:9 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) ── Error ('test-metaDigitise.R:21:2'): Checking specify_type works as expected.. ── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test-metaDigitise.R:21:9 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) ── Error ('test-misc_func.R:42:2'): Checking user_options.. ──────────────────── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. ├─testthat::expect_equal(...) at test-misc_func.R:42:9 2. │ └─testthat::quasi_label(enquo(object), label) 3. │ └─rlang::eval_bare(expr, quo_get_env(quo)) 4. └─metaDigitise (local) user_options_tester_func("question", c("a", "b", "c")) 5. └─testthat::with_mock(readline = function(question) "a", user_options(...)) at test-misc_func.R:35:9 6. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 7. └─lifecycle:::deprecate_stop0(msg) 8. └─rlang::cnd_signal(...) ── Error ('test-misc_func.R:52:2'): Checking user_unique.. ───────────────────── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. ├─testthat::expect_equal(...) at test-misc_func.R:52:9 2. │ └─testthat::quasi_label(enquo(object), label) 3. │ └─rlang::eval_bare(expr, quo_get_env(quo)) 4. └─testthat::with_mock(...) 5. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 6. └─lifecycle:::deprecate_stop0(msg) 7. └─rlang::cnd_signal(...) ── Error ('test-misc_func.R:71:2'): Checking user_numeric.. ──────────────────── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. ├─testthat::expect_equal(...) at test-misc_func.R:71:9 2. │ └─testthat::quasi_label(enquo(object), label) 3. │ └─rlang::eval_bare(expr, quo_get_env(quo)) 4. └─metaDigitise (local) user_numeric_tester_func("question", user_entry = "1") 5. └─testthat::with_mock(...) at test-misc_func.R:64:9 6. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 7. └─lifecycle:::deprecate_stop0(msg) 8. └─rlang::cnd_signal(...) ── Error ('test-misc_func.R:86:2'): Checking user_count.. ────────────────────── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. ├─testthat::expect_equal(...) at test-misc_func.R:86:9 2. │ └─testthat::quasi_label(enquo(object), label) 3. │ └─rlang::eval_bare(expr, quo_get_env(quo)) 4. └─metaDigitise (local) user_count_tester_func("question") 5. └─testthat::with_mock(readline = function(question) "1", user_count(...)) at test-misc_func.R:79:9 6. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 7. └─lifecycle:::deprecate_stop0(msg) 8. └─rlang::cnd_signal(...) ── Error ('test-misc_func.R:95:1'): Checking user_base.. ─────────────────────── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test-misc_func.R:95:1 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) ── Error ('test-misc_func.R:112:2'): Checking ask_variable.. ─────────────────── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. ├─testthat::expect_equal(...) at test-misc_func.R:112:9 2. │ └─testthat::quasi_label(enquo(object), label) 3. │ └─rlang::eval_bare(expr, quo_get_env(quo)) 4. └─metaDigitise (local) ask_variable_tester_func(plot_type = "scatterplot") 5. └─testthat::with_mock(readline = function(question) "x", ask_variable(...)) at test-misc_func.R:105:9 6. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 7. └─lifecycle:::deprecate_stop0(msg) 8. └─rlang::cnd_signal(...) ── Error ('test-misc_func.R:132:2'): Checking knownN.. ───────────────────────── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test-misc_func.R:132:9 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) ── Error ('test-point_extraction.R:9:2'): Checking point_extraction.. ────────── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. ├─testthat::expect_equal(...) at test-point_extraction.R:17:9 2. │ └─testthat::quasi_label(enquo(object), label) 3. │ └─rlang::eval_bare(expr, quo_get_env(quo)) 4. └─metaDigitise (local) point_extraction_tester_func(object = list(plot_type = "mean_error")) 5. └─testthat::with_mock(...) at test-point_extraction.R:9:9 6. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 7. └─lifecycle:::deprecate_stop0(msg) 8. └─rlang::cnd_signal(...) [ FAIL 14 | WARN 0 | SKIP 0 | PASS 35 ] Error: ! Test failures. Execution halted Package: mknapsack Check: tests New result: ERROR Running ‘testthat.R’ [2s/2s] Running the tests in ‘tests/testthat.R’ failed. Complete output: > suppressPackageStartupMessages({ + library(testthat) + library(data.table) + }) > > test_check("mknapsack") Loading required package: mknapsack Saving _problems/test-packing-153.R [ FAIL 1 | WARN 0 | SKIP 0 | PASS 21 ] ══ Failed tests ════════════════════════════════════════════════════════════════ ── Error ('test-packing.R:146:5'): solver / calls correct method based on the option value ── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test-packing.R:146:5 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) [ FAIL 1 | WARN 0 | SKIP 0 | PASS 21 ] Error: ! Test failures. Execution halted Package: moexer Check: tests New result: ERROR Running ‘testthat.R’ [2s/2s] Running the tests in ‘tests/testthat.R’ failed. Complete output: > library(testthat) > library(moexer) > > test_check("moexer") Saving _problems/test-candles-20.R Saving _problems/test-candles-31.R Saving _problems/test-candles-42.R Saving _problems/test-iss-6.R [ FAIL 4 | WARN 0 | SKIP 0 | PASS 0 ] ══ Failed tests ════════════════════════════════════════════════════════════════ ── Error ('test-candles.R:17:5'): Fetching candles works ─────────────────────── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test-candles.R:17:5 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) ── Error ('test-candles.R:28:5'): Specifying wrong symbols only returns an empty tibble ── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test-candles.R:28:5 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) ── Error ('test-candles.R:39:5'): Getting candle borders works ───────────────── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test-candles.R:39:5 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) ── Error ('test-iss.R:3:5'): Parsing a JSON ISS response works ───────────────── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test-iss.R:3:5 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) [ FAIL 4 | WARN 0 | SKIP 0 | PASS 0 ] Error: ! Test failures. Execution halted Package: MolgenisArmadillo Check: tests New result: ERROR Running ‘testthat.R’ [6s/6s] Running the tests in ‘tests/testthat.R’ failed. Complete output: > library(testthat) > library(tibble) > library(MolgenisArmadillo) > library(webmockr) > > test_check("MolgenisArmadillo") crul not installed, skipping enable HttrAdapter enabled! Httr2Adapter enabled! Saving _problems/test-object-46.R Saving _problems/test-object-86.R Attempting to install package [ 'C:/test/test.tar.gz' ] Attempting to install package [ 'C:/test/test.tar.gz' ] Package [ 'C:/test/test.tar.gz' ] installed Deleted 'project/folder2' Attaching package: 'dplyr' The following objects are masked from 'package:stats': filter, lag The following objects are masked from 'package:base': intersect, setdiff, setequal, union Renaming .csv column name `folder` to `source_folder`: please update your .csv file to silence this message. Renaming .csv column name `table` to `source_table`: please update your .csv file to silence this message. 'target_folder' not specified in .csv file: defaulting to source folder name 'target_table' not specified in .csv file: defaulting to source table name 'target_folder' not specified in .csv file: defaulting to source folder name 'target_table' not specified in .csv file: defaulting to source table name Argument `new project` has now been deprecated: please use `target_project` instead x View 'target_folder/target_table' failed with status '409': 'Project 'link-test222' already has an object 'core-variables/nonrep.alf' v View 'target_folder/target_table' successfully created Renaming .csv column name `folder` to `source_folder`: please update your .csv file to silence this message. Renaming .csv column name `table` to `source_table`: please update your .csv file to silence this message. 'target_folder' not specified in .csv file: defaulting to source folder name 'target_table' not specified in .csv file: defaulting to source table name [1] "project/folder1/obj1" "project/folder1/obj2" "project/folder2/obj3" [4] "project/folder2/obj4" [1] "project/folder1/obj1" "project/folder1/obj2" "project/folder2/obj3" [4] "project/folder2/obj4" Saving _problems/test-utils-17.R [ FAIL 4 | WARN 0 | SKIP 0 | PASS 237 ] ══ Failed tests ════════════════════════════════════════════════════════════════ ── Error ('test-object.R:30:3'): .upload_object handles errors ───────────────── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. i Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. ├─testthat::expect_error(...) at test-object.R:30:3 2. │ └─testthat:::expect_condition_matching_(...) 3. │ └─testthat:::quasi_capture(...) 4. │ ├─testthat (local) .capture(...) 5. │ │ └─base::withCallingHandlers(...) 6. │ └─rlang::eval_bare(quo_get_expr(.quo), quo_get_env(.quo)) 7. └─testthat::with_mock(...) 8. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 9. └─lifecycle:::deprecate_stop0(msg) 10. └─rlang::cnd_signal(...) ── Error ('test-object.R:70:3'): .upload_object uploads object ───────────────── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. i Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. ├─testthat::expect_message(...) at test-object.R:70:3 2. │ └─testthat:::expect_condition_matching_(...) 3. │ └─testthat:::quasi_capture(...) 4. │ ├─testthat (local) .capture(...) 5. │ │ └─base::withCallingHandlers(...) 6. │ └─rlang::eval_bare(quo_get_expr(.quo), quo_get_env(.quo)) 7. └─testthat::with_mock(...) 8. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 9. └─lifecycle:::deprecate_stop0(msg) 10. └─rlang::cnd_signal(...) ── Error ('test-object.R:310:3'): .load_object loads the object from file ────── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. i Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. ├─testthat::expect_silent(...) at test-object.R:310:3 2. │ └─testthat:::quasi_capture(enquo(object), NULL, evaluate_promise) 3. │ ├─testthat (local) .capture(...) 4. │ │ ├─withr::with_output_sink(...) 5. │ │ │ └─base::force(code) 6. │ │ ├─base::withCallingHandlers(...) 7. │ │ └─base::withVisible(code) 8. │ └─rlang::eval_bare(quo_get_expr(.quo), quo_get_env(.quo)) 9. └─testthat::with_mock(...) 10. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 11. └─lifecycle:::deprecate_stop0(msg) 12. └─rlang::cnd_signal(...) ── Error ('test-utils.R:11:3'): .handle_request_error handles 500 ────────────── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. i Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test-utils.R:11:3 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) [ FAIL 4 | WARN 0 | SKIP 0 | PASS 237 ] Error: ! Test failures. Execution halted Package: NasdaqDataLink Check: tests New result: ERROR Running ‘testthat.R’ [2s/2s] Running the tests in ‘tests/testthat.R’ failed. Complete output: > library(testthat) > test_check("NasdaqDataLink") Loading required package: NasdaqDataLink Loading required package: xts Loading required package: zoo Attaching package: 'zoo' The following objects are masked from 'package:base': as.Date, as.Date.numeric Saving _problems/test-api-16.R Saving _problems/test-database-21.R Saving _problems/test-datatable-22.R Saving _problems/test-get-38.R Saving _problems/test-pointintime-22.R Saving _problems/test-search-18.R [ FAIL 6 | WARN 0 | SKIP 2 | PASS 4 ] ══ Skipped tests (2) ═══════════════════════════════════════════════════════════ • empty test (2): , ══ Failed tests ════════════════════════════════════════════════════════════════ ── Error ('test-api.r:6:1'): (code run outside of `test_that()`) ─────────────── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test-api.r:6:1 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) ── Error ('test-database.r:11:1'): (code run outside of `test_that()`) ───────── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test-database.r:11:1 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) ── Error ('test-datatable.r:8:1'): (code run outside of `test_that()`) ───────── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test-datatable.r:8:1 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) ── Error ('test-get.r:23:1'): (code run outside of `test_that()`) ────────────── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test-get.r:23:1 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) ── Error ('test-pointintime.r:8:1'): (code run outside of `test_that()`) ─────── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test-pointintime.r:8:1 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) ── Error ('test-search.r:4:1'): (code run outside of `test_that()`) ──────────── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test-search.r:4:1 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) [ FAIL 6 | WARN 0 | SKIP 2 | PASS 4 ] Error: ! Test failures. Execution halted Package: nhdplusTools Check: tests New result: ERROR Running ‘testthat.R’ [53s/36s] Running the tests in ‘tests/testthat.R’ failed. Complete output: > library("testthat") > > test_check("nhdplusTools") Loading required package: nhdplusTools Starting 2 test processes. > test_02_subset.R: All intersections performed in latitude/longitude. > test_02_subset.R: All intersections performed in latitude/longitude. > test_02_subset.R: All intersections performed in latitude/longitude. > test_02_subset.R: All intersections performed in latitude/longitude. Saving _problems/test_02_subset_extras-75.R [ FAIL 1 | WARN 0 | SKIP 58 | PASS 315 ] ══ Skipped tests (58) ══════════════════════════════════════════════════════════ • On CRAN (58): 'test_00_plot_bbox_nhdplus.R:5:3', 'test_00_plot_local_extra_nhdplus.R:9:3', 'test_00_plot_local_nhdplus.R:9:3', 'test_00_plot_integration_nhdplus.R:6:3', 'test_01_get_nldi.R:5:3', 'test_01_get_nldi.R:25:3', 'test_01_get_nldi.R:85:3', 'test_01_get_nldi.R:122:3', 'test_01_get_nldi.R:140:3', 'test_01_get_nldi.R:176:3', 'test_01_get_nldi.R:224:3', 'test_00_plot_nhdplus.R:6:3', 'test_00_plot_nhdplus.R:90:3', 'test_00_plot_nhdplus.R:134:3', 'test_00_plot_nhdplus.R:164:3', 'test_00_plot_nhdplus.R:201:3', 'test_02_subset.R:135:3', 'test_02_subset.R:191:3', 'test_03_get_functions.R:21:3', 'test_03_get_functions.R:48:3', 'test_03_get_functions.R:71:3', 'test_03_get_functions.R:122:3', 'test_03_get_functions.R:193:3', 'test_03_get_functions.R:209:3', 'test_03_get_functions.R:222:3', 'test_03_get_functions.R:240:3', 'test_03_get_functions.R:247:3', 'test_arcrest.R:58:3', 'test_02_subset_extras.R:94:3', 'test_02_subset_extras.R:149:3', 'test_geoconnex.R:3:3', 'test_geoconnex.R:14:3', 'test_get_3dhp.R:3:3', 'test_get_codes.R:4:3', 'test_get_nhdphr.R:3:3', 'test_get_nhdplus.R:8:3', 'test_get_nhdplus.R:29:3', 'test_get_nhdplus.R:49:3', 'test_get_nhdplushr.R:3:3', 'test_get_nhdplushr.R:23:3', 'test_get_nhdplushr.R:53:3', 'test_get_nhdplushr.R:80:3', 'test_get_nhdplushr.R:101:3', 'test_get_nhdplushr.R:147:3', 'test_get_nhdplushr.R:174:3', 'test_get_nhdplushr.R:196:3', 'test_get_vaa.R:4:3', 'test_get_vaa.R:38:3', 'test_index.R:13:5', 'test_index.R:61:3', 'test_map_nhdplus.R:4:3', 'test_navigate.R:4:3', 'test_navigate.R:71:3', 'test_nhdplusTools.R:41:3', 'test_nhdplusTools.R:50:3', 'test_rescale_catchments.R:3:3', 'test_run_plus_attributes.R:20:3', 'test_get_path.R:69:3' ══ Failed tests ════════════════════════════════════════════════════════════════ ── Error ('test_02_subset_extras.R:75:3'): by rpu ────────────────────────────── Error in `expect(nrow(out), 267)`: `ok` must be `TRUE` or `FALSE`, not the number 267. Backtrace: ▆ 1. └─testthat::expect(ok = nrow(out)) at test_02_subset_extras.R:75:3 2. └─testthat:::check_bool(ok) 3. └─testthat:::stop_input_type(...) 4. └─rlang::abort(message, ..., call = call, arg = arg) [ FAIL 1 | WARN 0 | SKIP 58 | PASS 315 ] Error: ! Test failures. Execution halted Package: nhlapi Check: tests New result: ERROR Running ‘testthat.R’ [6s/7s] Running the tests in ‘tests/testthat.R’ failed. Complete output: > library(testthat) > library(nhlapi) > > test_check("nhlapi") Saving _problems/test.nhl_get_data-50.R Saving _problems/test.nhl_get_data-62.R Saving _problems/test.nhl_get_data-79.R Saving _problems/test.nhl_get_data-122.R Saving _problems/test.nhl_get_data-155.R Saving _problems/test.nhl_md_endpoints-11.R Saving _problems/test.nhl_md_endpoints-23.R Saving _problems/test.nhl_md_endpoints-35.R Saving _problems/test.nhl_md_endpoints-47.R Saving _problems/test.nhl_md_endpoints-59.R Saving _problems/test.nhl_md_endpoints-71.R Saving _problems/test.nhl_md_endpoints-83.R Saving _problems/test.nhl_process_results-35.R Saving _problems/test.nhl_teams_rosters-32.R Saving _problems/test.nhl_teams_schedule-14.R Saving _problems/test.nhl_teams_schedule-29.R Saving _problems/test.nhl_teams_stats-20.R [ FAIL 17 | WARN 0 | SKIP 38 | PASS 147 ] ══ Skipped tests (38) ══════════════════════════════════════════════════════════ • On CRAN (37): 'test.nhl_awards.R:5:5', 'test.nhl_conferences.R:5:5', 'test.nhl_divisions.R:5:5', 'test.nhl_draft_prospects.R:5:5', 'test.nhl_drafts.R:5:5', 'test.nhl_games.R:5:5', 'test.nhl_games.R:18:5', 'test.nhl_games.R:31:5', 'test.nhl_games.R:44:5', 'test.nhl_get_data.R:5:5', 'test.nhl_get_data.R:17:5', 'test.nhl_get_data.R:32:5', 'test.nhl_players.R:5:5', 'test.nhl_players_seasons.R:5:5', 'test.nhl_players_seasons.R:21:5', 'test.nhl_players_seasons.R:37:5', 'test.nhl_players_seasons.R:63:5', 'test.nhl_players_seasons.R:83:5', 'test.nhl_players_seasons.R:95:5', 'test.nhl_schedule.R:5:5', 'test.nhl_schedule.R:22:5', 'test.nhl_schedule.R:48:5', 'test.nhl_schedule.R:78:5', 'test.nhl_schedule.R:109:5', 'test.nhl_schedule.R:142:5', 'test.nhl_seasons.R:5:5', 'test.nhl_standings.R:5:5', 'test.nhl_standings.R:28:5', 'test.nhl_standings.R:53:5', 'test.nhl_standings.R:83:5', 'test.nhl_teams.R:5:5', 'test.nhl_teams_rosters.R:5:5', 'test.nhl_tournaments.R:5:5', 'test.nhl_tournaments.R:18:5', 'test.nhl_tournaments.R:31:5', 'test.nhl_utils.R:303:5', 'test.nhl_venues.R:5:5' • skipSelectedTests is TRUE (1): 'test.nhl_plot_rink.R:16:5' ══ Failed tests ════════════════════════════════════════════════════════════════ ── Error ('test.nhl_get_data.R:47:5'): nhl_from_json ─────────────────────────── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test.nhl_get_data.R:47:5 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) ── Error ('test.nhl_get_data.R:56:5'): nhl_from_json ─────────────────────────── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test.nhl_get_data.R:56:5 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) ── Error ('test.nhl_get_data.R:68:5'): nhl_from_json retries and fails n times ── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test.nhl_get_data.R:68:5 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) ── Error ('test.nhl_get_data.R:86:5'): nhl_from_json retries then succeeds ───── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test.nhl_get_data.R:86:5 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) ── Error ('test.nhl_get_data.R:133:5'): nhl_from_json no retries for noRetryPatt, only for valid ── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test.nhl_get_data.R:133:5 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) ── Error ('test.nhl_md_endpoints.R:5:5'): nhl_md_game_types ──────────────────── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. ├─testthat::expect_equal(...) at test.nhl_md_endpoints.R:5:5 2. │ └─testthat::quasi_label(enquo(object), label) 3. │ └─rlang::eval_bare(expr, quo_get_env(quo)) 4. └─testthat::with_mock(nhl_get_data = mock_identity, nhl_md_game_types()) 5. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 6. └─lifecycle:::deprecate_stop0(msg) 7. └─rlang::cnd_signal(...) ── Error ('test.nhl_md_endpoints.R:17:5'): nhl_md_game_statuses ──────────────── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. ├─testthat::expect_equal(...) at test.nhl_md_endpoints.R:17:5 2. │ └─testthat::quasi_label(enquo(object), label) 3. │ └─rlang::eval_bare(expr, quo_get_env(quo)) 4. └─testthat::with_mock(nhl_get_data = mock_identity, nhl_md_game_statuses()) 5. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 6. └─lifecycle:::deprecate_stop0(msg) 7. └─rlang::cnd_signal(...) ── Error ('test.nhl_md_endpoints.R:29:5'): nhl_md_play_types ─────────────────── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. ├─testthat::expect_equal(...) at test.nhl_md_endpoints.R:29:5 2. │ └─testthat::quasi_label(enquo(object), label) 3. │ └─rlang::eval_bare(expr, quo_get_env(quo)) 4. └─testthat::with_mock(nhl_get_data = mock_identity, nhl_md_play_types()) 5. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 6. └─lifecycle:::deprecate_stop0(msg) 7. └─rlang::cnd_signal(...) ── Error ('test.nhl_md_endpoints.R:41:5'): nhl_md_tournament_types ───────────── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. ├─testthat::expect_equal(...) at test.nhl_md_endpoints.R:41:5 2. │ └─testthat::quasi_label(enquo(object), label) 3. │ └─rlang::eval_bare(expr, quo_get_env(quo)) 4. └─testthat::with_mock(nhl_get_data = mock_identity, nhl_md_tournament_types()) 5. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 6. └─lifecycle:::deprecate_stop0(msg) 7. └─rlang::cnd_signal(...) ── Error ('test.nhl_md_endpoints.R:53:5'): nhl_md_standings_types ────────────── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. ├─testthat::expect_equal(...) at test.nhl_md_endpoints.R:53:5 2. │ └─testthat::quasi_label(enquo(object), label) 3. │ └─rlang::eval_bare(expr, quo_get_env(quo)) 4. └─testthat::with_mock(nhl_get_data = mock_identity, nhl_md_standings_types()) 5. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 6. └─lifecycle:::deprecate_stop0(msg) 7. └─rlang::cnd_signal(...) ── Error ('test.nhl_md_endpoints.R:65:5'): nhl_md_stat_types ─────────────────── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. ├─testthat::expect_equal(...) at test.nhl_md_endpoints.R:65:5 2. │ └─testthat::quasi_label(enquo(object), label) 3. │ └─rlang::eval_bare(expr, quo_get_env(quo)) 4. └─testthat::with_mock(nhl_get_data = mock_identity, nhl_md_stat_types()) 5. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 6. └─lifecycle:::deprecate_stop0(msg) 7. └─rlang::cnd_signal(...) ── Error ('test.nhl_md_endpoints.R:77:5'): nhl_md_event_types ────────────────── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. ├─testthat::expect_equal(...) at test.nhl_md_endpoints.R:77:5 2. │ └─testthat::quasi_label(enquo(object), label) 3. │ └─rlang::eval_bare(expr, quo_get_env(quo)) 4. └─testthat::with_mock(nhl_get_data = mock_identity, nhl_md_event_types()) 5. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 6. └─lifecycle:::deprecate_stop0(msg) 7. └─rlang::cnd_signal(...) ── Error ('test.nhl_process_results.R:29:5'): Warn but return if failed rbindlist ── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. ├─testthat::expect_warning(...) at test.nhl_process_results.R:29:5 2. │ └─testthat:::quasi_capture(...) 3. │ ├─testthat (local) .capture(...) 4. │ │ └─base::withCallingHandlers(...) 5. │ └─rlang::eval_bare(quo_get_expr(.quo), quo_get_env(.quo)) 6. └─testthat::with_mock(...) 7. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 8. └─lifecycle:::deprecate_stop0(msg) 9. └─rlang::cnd_signal(...) ── Error ('test.nhl_teams_rosters.R:17:5'): Roster for 2 teams and 2 seasons ─── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. ├─testthat::expect_equal(...) at test.nhl_teams_rosters.R:17:5 2. │ └─testthat::quasi_label(enquo(object), label) 3. │ └─rlang::eval_bare(expr, quo_get_env(quo)) 4. └─testthat::with_mock(...) 5. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 6. └─lifecycle:::deprecate_stop0(msg) 7. └─rlang::cnd_signal(...) ── Error ('test.nhl_teams_schedule.R:5:5'): Next match schedule for 2 teams ──── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. ├─testthat::expect_equal(...) at test.nhl_teams_schedule.R:5:5 2. │ └─testthat::quasi_label(enquo(object), label) 3. │ └─rlang::eval_bare(expr, quo_get_env(quo)) 4. └─testthat::with_mock(nhl_teams = mock_return, nhl_teams_shedule_next(teamIds = 1:2)) 5. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 6. └─lifecycle:::deprecate_stop0(msg) 7. └─rlang::cnd_signal(...) ── Error ('test.nhl_teams_schedule.R:20:5'): Next match schedule for 2 teams ─── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. ├─testthat::expect_equal(...) at test.nhl_teams_schedule.R:20:5 2. │ └─testthat::quasi_label(enquo(object), label) 3. │ └─rlang::eval_bare(expr, quo_get_env(quo)) 4. └─testthat::with_mock(nhl_teams = mock_return, nhl_teams_shedule_previous(teamIds = 1:2)) 5. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 6. └─lifecycle:::deprecate_stop0(msg) 7. └─rlang::cnd_signal(...) ── Error ('test.nhl_teams_stats.R:5:5'): Statas for 2 teams and 2 seasons ────── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. ├─testthat::expect_equal(...) at test.nhl_teams_stats.R:5:5 2. │ └─testthat::quasi_label(enquo(object), label) 3. │ └─rlang::eval_bare(expr, quo_get_env(quo)) 4. └─testthat::with_mock(...) 5. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 6. └─lifecycle:::deprecate_stop0(msg) 7. └─rlang::cnd_signal(...) [ FAIL 17 | WARN 0 | SKIP 38 | PASS 147 ] Error: ! Test failures. Execution halted Package: nodiv Check: tests New result: ERROR Running ‘testthat.R’ [11s/11s] Running the tests in ‘tests/testthat.R’ failed. Complete output: > library(testthat) > library(nodiv) > > test_check("nodiv") Saving _problems/test_adding-4.R | | | 0% | |=== | 5% | |====== | 9% | |========== | 14% | |============= | 18% | |================ | 23% | |=================== | 27% | |====================== | 32% | |========================= | 36% | |============================= | 41% | |================================ | 45% | |=================================== | 50% | |====================================== | 55% | |========================================= | 59% | |============================================= | 64% | |================================================ | 68% | |=================================================== | 73% | |====================================================== | 77% | |========================================================= | 82% | |============================================================ | 86% | |================================================================ | 91% | |=================================================================== | 95% | |======================================================================| 100%[ FAIL 1 | WARN 0 | SKIP 0 | PASS 63 ] ══ Failed tests ════════════════════════════════════════════════════════════════ ── Error ('test_adding.R:4:3'): add shape to object ──────────────────────────── Error in `expect(is.null(coquettes$shape))`: `failure_message` must be a character vector, not absent. Backtrace: ▆ 1. └─testthat::expect(failure_message = ) at test_adding.R:4:3 2. └─testthat:::check_character(failure_message) 3. └─testthat:::stop_input_type(...) 4. └─rlang::abort(message, ..., call = call, arg = arg) [ FAIL 1 | WARN 0 | SKIP 0 | PASS 63 ] Error: ! Test failures. Execution halted Package: operator.tools Check: tests New result: ERROR Running ‘testthat.R’ [2s/2s] Running the tests in ‘tests/testthat.R’ failed. Complete output: > library(testthat) > library(operator.tools) > > test_check("operator.tools") Saving _problems/test-operators-11.R [ FAIL 1 | WARN 0 | SKIP 0 | PASS 27 ] ══ Failed tests ════════════════════════════════════════════════════════════════ ── Error ('test-operators.R:6:1'): opetators ─────────────────────────────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(...) at test-operators.R:6:1 [ FAIL 1 | WARN 0 | SKIP 0 | PASS 27 ] Error: ! Test failures. Execution halted Package: optigrab Check: tests New result: ERROR Running ‘testthat.R’ [1s/1s] Running the tests in ‘tests/testthat.R’ failed. Complete output: > library(testthat) > library(optigrab) > > test_check("optigrab") Saving _problems/test-opt_expand-98.R [ FAIL 1 | WARN 0 | SKIP 0 | PASS 106 ] ══ Failed tests ════════════════════════════════════════════════════════════════ ── Error ('test-opt_expand.r:98:1'): (code run outside of `test_that()`) ─────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.character(expanded), is_true()) at test-opt_expand.r:98:1 [ FAIL 1 | WARN 0 | SKIP 0 | PASS 106 ] Error: ! Test failures. Execution halted Package: otargen Check: tests New result: ERROR Running ‘testthat.R’ [3s/33s] Running the tests in ‘tests/testthat.R’ failed. Complete output: > library(testthat) > library(otargen) > > test_check("otargen") \ Sending GraphQL request... Saving _problems/test_gwasCredibleSetsQuery-14.R x Sending GraphQL request... [30.5s] [ FAIL 1 | WARN 0 | SKIP 30 | PASS 1 ] ══ Skipped tests (30) ══════════════════════════════════════════════════════════ • On CRAN (30): 'test_GWASColocQuery.R:2:3', 'test_adverseEventsQuery.R:2:3', 'test_chemblQuery.R:2:3', 'test_clinVarQuery.R:2:3', 'test_compGenomicsQuery.R:2:3', 'test_depMapQuery.R:2:3', 'test_europePMCQuery.R:2:3', 'test_geneBurdenQuery.R:2:3', 'test_geneOntologyQuery.R:2:3', 'test_geneticConstraintQuery.R:2:3', 'test_genomicsEnglandQuery.R:2:3', 'test_hallmarksQuery.R:2:3', 'test_interactionsQuery.R:2:3', 'test_knownDrugsChemblQuery.R:2:3', 'test_knownDrugsGeneQuery.R:2:3', 'test_mechanismsOfActionQuery.R:2:3', 'test_mousePhenotypesQuery.R:2:3', 'test_orphanetQuery.R:2:3', 'test_pathwaysQuery.R:2:3', 'test_pharmacogenomicsChemblQuery.R:2:3', 'test_pharmacogenomicsGeneQuery.R:2:3', 'test_pharmacogenomicsVariantQuery.R:2:3', 'test_qtlCredibleSetsQuery.R:2:3', 'test_safetyQuery.R:2:3', 'test_sharedTraitStudiesQuery.R:2:3', 'test_uniProtVariantsQuery.R:2:3', 'test_uniprotLiteratureQuery.R:2:3', 'test_variantEffectPredictorQuery.R:2:3', 'test_variantEffectQuery.R:2:3', 'test_variantsQuery.R:2:3' ══ Failed tests ════════════════════════════════════════════════════════════════ ── Error ('test_gwasCredibleSetsQuery.R:10:3'): gwasCredibleSetsQuery returns expected results ── Error in `gwasCredibleSetsQuery(ensemblId = ensemblId, efoId = efoId, size = size)`: GraphQL query failed. HTTP status: 502 Backtrace: ▆ 1. └─otargen::gwasCredibleSetsQuery(...) at test_gwasCredibleSetsQuery.R:10:3 [ FAIL 1 | WARN 0 | SKIP 30 | PASS 1 ] Error: ! Test failures. Execution halted Package: owmr Check: tests New result: ERROR Running ‘testthat.R’ [2s/3s] Running the tests in ‘tests/testthat.R’ failed. Complete output: > library(testthat) > library(owmr) owmr 0.8.2 another crazy way to talk to OpenWeatherMap's API Documentation: type ?owmr or https://crazycapivara.github.io/owmr/ Issues, notes and bleeding edge: https://github.com/crazycapivara/owmr/ It is recommended that you store your api key in an environment variable called OWM_API_KEY. > > test_check("owmr") Saving _problems/test_current_mocks-30.R Saving _problems/test_forecast-29.R [ FAIL 2 | WARN 3 | SKIP 0 | PASS 28 ] ══ Failed tests ════════════════════════════════════════════════════════════════ ── Error ('test_current_mocks.R:8:3'): current weather data ──────────────────── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test_current_mocks.R:8:3 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) ── Error ('test_forecast.R:8:3'): forecast data ──────────────────────────────── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test_forecast.R:8:3 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) [ FAIL 2 | WARN 3 | SKIP 0 | PASS 28 ] Error: ! Test failures. Execution halted Package: oxcAAR Check: tests New result: ERROR Running ‘testthat.R’ [5s/5s] Running the tests in ‘tests/testthat.R’ failed. Complete output: > library(testthat) > library(oxcAAR) > > test_check("oxcAAR") Saving _problems/test_calibrate-9.R Saving _problems/test_calibrate-62.R Saving _problems/test_oxcalsumsim-9.R Saving _problems/test_oxcalsumsim-43.R Saving _problems/test_simulate-9.R Saving _problems/test_simulate-61.R [ FAIL 6 | WARN 2 | SKIP 1 | PASS 50 ] ══ Skipped tests (1) ═══════════════════════════════════════════════════════════ • On CRAN (1): 'test_utility_functions.R:30:3' ══ Failed tests ════════════════════════════════════════════════════════════════ ── Error ('test_calibrate.R:4:3'): oxcalCalibrate produces error given wrong oxcal result file ── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test_calibrate.R:4:3 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) ── Error ('test_calibrate.R:12:1'): (code run outside of `test_that()`) ──────── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test_calibrate.R:12:1 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) ── Error ('test_oxcalsumsim.R:4:3'): oxcalSumSim produces error given wrong oxcal result file ── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test_oxcalsumsim.R:4:3 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) ── Error ('test_oxcalsumsim.R:12:1'): (code run outside of `test_that()`) ────── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test_oxcalsumsim.R:12:1 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) ── Error ('test_simulate.R:4:3'): oxcalSimulate produces error given wrong oxcal result file ── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test_simulate.R:4:3 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) ── Error ('test_simulate.R:12:1'): (code run outside of `test_that()`) ───────── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test_simulate.R:12:1 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) [ FAIL 6 | WARN 2 | SKIP 1 | PASS 50 ] Error: ! Test failures. Execution halted Package: passport Check: tests New result: ERROR Running ‘testthat.R’ [1s/2s] Running the tests in ‘tests/testthat.R’ failed. Complete output: > library(testthat) > library(passport) > > test_check("passport") Saving _problems/test_parse_country-49.R [ FAIL 1 | WARN 4 | SKIP 1 | PASS 37 ] ══ Skipped tests (1) ═══════════════════════════════════════════════════════════ • On CRAN (1): 'test_parse_country.R:91:5' ══ Failed tests ════════════════════════════════════════════════════════════════ ── Failure ('test_parse_country.R:43:5'): parsing country names with simulated geocoding APIs works ── `with_mock(...)` threw an error with unexpected message. Expected match: "jsonlite" Actual message: "`with_mock()` was deprecated in testthat 3.2.0 and is now defunct.\nℹ Please use `with_mocked_bindings()` instead." Backtrace: ▆ 1. ├─testthat::expect_error(...) at test_parse_country.R:43:5 2. │ └─testthat:::quasi_capture(...) 3. │ ├─testthat (local) .capture(...) 4. │ │ └─base::withCallingHandlers(...) 5. │ └─rlang::eval_bare(quo_get_expr(.quo), quo_get_env(.quo)) 6. └─testthat::with_mock(...) 7. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 8. └─lifecycle:::deprecate_stop0(msg) 9. └─rlang::cnd_signal(...) [ FAIL 1 | WARN 4 | SKIP 1 | PASS 37 ] Error: ! Test failures. Execution halted Package: patrick Check: tests New result: ERROR Running ‘testthat.R’ [2s/2s] Running the tests in ‘tests/testthat.R’ failed. Complete output: > # Copyright 2018 Google LLC > # > # Licensed under the Apache License, Version 2.0 (the "License"); > # you may not use this file except in compliance with the License. > # You may obtain a copy of the License at > # > # http://www.apache.org/licenses/LICENSE-2.0 > # > # Unless required by applicable law or agreed to in writing, software > # distributed under the License is distributed on an "AS IS" BASIS, > # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. > # See the License for the specific language governing permissions and > # limitations under the License. > > library(patrick) > library(testthat) > > test_check("patrick") Saving _problems/test-with_parameters-22.R Saving _problems/test-with_parameters-22.R [ FAIL 2 | WARN 0 | SKIP 0 | PASS 24 ] ══ Failed tests ════════════════════════════════════════════════════════════════ ── Failure ('test-with_parameters.R:22:7'): Running tests: fail ──────────────── Expected failure message to match regexp "`case` (isn't true|is not TRUE)". Actual message: x | Expected `case` to be TRUE. | Differences: | `actual`: FALSE | `expected`: TRUE | Backtrace: ▆ 1. ├─rlang::eval_tidy(code, args) 2. └─testthat::expect_failure(testthat::expect_true(case), failure_message) at test-with_parameters.R:22:7 ── Failure ('test-with_parameters.R:22:7'): Running tests: null ──────────────── Expected failure message to match regexp "`case` (isn't true|is not TRUE)". Actual message: x | Expected `case` to be TRUE. | Differences: | `actual` is NULL | `expected` is a logical vector (TRUE) | Backtrace: ▆ 1. ├─rlang::eval_tidy(code, args) 2. └─testthat::expect_failure(testthat::expect_true(case), failure_message) at test-with_parameters.R:22:7 [ FAIL 2 | WARN 0 | SKIP 0 | PASS 24 ] Error: ! Test failures. Execution halted Package: PCRedux Check: tests New result: ERROR Running ‘testthat.R’ [8s/8s] Running the tests in ‘tests/testthat.R’ failed. Complete output: > library(testthat) > library(PCRedux) > > test_check("PCRedux") Saving _problems/test_encu-11.R Saving _problems/test_head2tailratio-12.R Saving _problems/test_hookreg-12.R Saving _problems/test_hookregNL-12.R Saving _problems/test_mblrr-10.R Saving _problems/test_pcrfit_single-9.R Saving _problems/test_performeR-11.R Saving _problems/test_qPCR2fdata-11.R [ FAIL 8 | WARN 0 | SKIP 0 | PASS 23 ] ══ Failed tests ════════════════════════════════════════════════════════════════ ── Error ('test_encu.R:11:3'): encu is a function to calculate numerous features from amplification curve data from a quantitative PCR experiment ── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(res$hookreg_hook == 0, is_true()) at test_encu.R:11:3 ── Error ('test_head2tailratio.R:12:3'): head2tailratio gives the correct dimensions and properties ── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(res <= 3.589284, is_true()) at test_head2tailratio.R:12:3 ── Error ('test_hookreg.R:12:3'): hookreg gives the correct dimensions and properties ── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(res_hookreg_positve[["hook"]] == 1, is_true()) at test_hookreg.R:12:3 ── Error ('test_hookregNL.R:12:3'): hookregNL gives the correct dimensions and properties ── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(...) at test_hookregNL.R:12:3 ── Error ('test_mblrr.R:10:3'): mblrr gives the correct dimensions and properties ── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(length(res) == 6, is_true()) at test_mblrr.R:10:3 ── Error ('test_pcrfit_single.R:9:3'): pcrfit_single gives the correct dimensions and properties ── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(...) at test_pcrfit_single.R:9:3 ── Error ('test_performeR.R:11:3'): performeR gives the correct dimensions and properties ── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(...) at test_performeR.R:11:3 ── Error ('test_qPCR2fdata.R:11:3'): qPCR2fdata gives the correct dimensions and properties ── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(...) at test_qPCR2fdata.R:11:3 [ FAIL 8 | WARN 0 | SKIP 0 | PASS 23 ] Error: ! Test failures. Execution halted Package: phyloraster Check: tests New result: ERROR Running ‘testthat.R’ [51s/51s] Running the tests in ‘tests/testthat.R’ failed. Complete output: > library(testthat) > library(phyloraster) > > test_check("phyloraster") Saving _problems/test-delta-grid-81.R Saving _problems/test-geo.phylo-144.R Saving _problems/test-geo.phylo-167.R Saving _problems/test-rast-ed-ses-121.R Saving _problems/test-rast.pd.ses-120.R Saving _problems/test-rast.pe.ses-102.R Saving _problems/test-shp2rast-79.R Saving _problems/test-shp2rast-101.R [ FAIL 8 | WARN 0 | SKIP 0 | PASS 84 ] ══ Failed tests ════════════════════════════════════════════════════════════════ ── Error ('test-delta-grid.R:80:3'): Raster is saved when filename is provided ── Error in `expect(phyloraster::delta.grid(riq.pres, riq.fut, filename = temp), ok = T)`: `failure_message` must be a character vector, not a object. Backtrace: ▆ 1. └─testthat::expect(failure_message = phyloraster::delta.grid(riq.pres, riq.fut, filename = temp)) at test-delta-grid.R:80:3 2. └─testthat:::check_character(failure_message) 3. └─testthat:::stop_input_type(...) 4. └─rlang::abort(message, ..., call = call, arg = arg) ── Error ('test-geo.phylo.R:144:3'): arguments are calculated when is missing and the tree is provided ── Error in `expect(geo.phylo(data$x, tree), ok = T)`: `failure_message` must be a character vector, not a object. Backtrace: ▆ 1. └─testthat::expect(failure_message = geo.phylo(data$x, tree)) at test-geo.phylo.R:144:3 2. └─testthat:::check_character(failure_message) 3. └─testthat:::stop_input_type(...) 4. └─rlang::abort(message, ..., call = call, arg = arg) ── Error ('test-geo.phylo.R:163:3'): names are reordened in the function geo.phylo ── Error in `expect(geo.phylo(data$x, tree, inv.R = inv.R, edge.path = data$edge.path[sample(1:nrow(data$edge.path)), ], branch.length = data$branch.length, n.descen = data$n.descendants), ok = T)`: `failure_message` must be a character vector, not a object. Backtrace: ▆ 1. └─testthat::expect(failure_message = geo.phylo(...)) at test-geo.phylo.R:163:3 2. └─testthat:::check_character(failure_message) 3. └─testthat:::stop_input_type(...) 4. └─rlang::abort(message, ..., call = call, arg = arg) ── Error ('test-rast-ed-ses.R:116:3'): function compute branch lenght when tree is supplied ── Error in `expect(rast.ed.ses(x = x, tree = tree, spat_alg = "bootspat_str", spat_alg_args = list(rprob = NULL, rich = NULL, fr_prob = NULL), aleats = 5), ok = T)`: `failure_message` must be a character vector, not a object. Backtrace: ▆ 1. └─testthat::expect(failure_message = rast.ed.ses(...)) at test-rast-ed-ses.R:116:3 2. └─testthat:::check_character(failure_message) 3. └─testthat:::stop_input_type(...) 4. └─rlang::abort(message, ..., call = call, arg = arg) ── Error ('test-rast.pd.ses.R:115:3'): function compute branch lenght when tree is supplied ── Error in `expect(rast.pd.ses(x = x, tree = tree, spat_alg = "bootspat_str", spat_alg_args = list(rprob = NULL, rich = NULL, fr_prob = NULL), aleats = 5), ok = T)`: `failure_message` must be a character vector, not a object. Backtrace: ▆ 1. └─testthat::expect(failure_message = rast.pd.ses(...)) at test-rast.pd.ses.R:115:3 2. └─testthat:::check_character(failure_message) 3. └─testthat:::stop_input_type(...) 4. └─rlang::abort(message, ..., call = call, arg = arg) ── Error ('test-rast.pe.ses.R:96:3'): function compute branch lenght when tree is supplied ── Error in `expect(rast.pe.ses(x = x, tree = tree, spat_alg = "bootspat_str", metric = "pe", spat_alg_args = list(rprob = NULL, rich = NULL, fr_prob = NULL), aleats = 5), ok = T)`: `failure_message` must be a character vector, not a object. Backtrace: ▆ 1. └─testthat::expect(failure_message = rast.pe.ses(...)) at test-rast.pe.ses.R:96:3 2. └─testthat:::check_character(failure_message) 3. └─testthat:::stop_input_type(...) 4. └─rlang::abort(message, ..., call = call, arg = arg) ── Error ('test-shp2rast.R:77:3'): function runs ok when a mask is applied ───── Error in `expect(phyloraster::shp2rast(shp, y = coun.rast, sps.col = "BINOMIAL", ymask = TRUE, background = 0), ok = T)`: `failure_message` must be a character vector, not a object. Backtrace: ▆ 1. └─testthat::expect(failure_message = phyloraster::shp2rast(...)) at test-shp2rast.R:77:3 2. └─testthat:::check_character(failure_message) 3. └─testthat:::stop_input_type(...) 4. └─rlang::abort(message, ..., call = call, arg = arg) ── Error ('test-shp2rast.R:100:3'): Raster is saved when filename is provided ── Error in `expect(shp2rast(shp, y = coun.rast, sps.col = "BINOMIAL", background = 0, filename = temp), ok = T)`: `failure_message` must be a character vector, not a object. Backtrace: ▆ 1. └─testthat::expect(failure_message = shp2rast(...)) at test-shp2rast.R:100:3 2. └─testthat:::check_character(failure_message) 3. └─testthat:::stop_input_type(...) 4. └─rlang::abort(message, ..., call = call, arg = arg) [ FAIL 8 | WARN 0 | SKIP 0 | PASS 84 ] Error: ! Test failures. Execution halted Package: pocketapi Check: tests New result: ERROR Running ‘testthat.R’ [5s/5s] Running the tests in ‘tests/testthat.R’ failed. Complete output: > library(testthat) > library(pocketapi) > > test_check("pocketapi") Saving _problems/test_pocket_add-43.R Saving _problems/test_pocket_archive-28.R Saving _problems/test_pocket_archive-44.R Saving _problems/test_pocket_delete-29.R Saving _problems/test_pocket_delete-44.R Saving _problems/test_pocket_delete-58.R Saving _problems/test_pocket_favorite-28.R Saving _problems/test_pocket_tag-37.R Saving _problems/test_pocket_tag-51.R Saving _problems/test_pocket_tag-71.R Saving _problems/test_pocket_tag-91.R Saving _problems/test_pocket_tag-112.R Saving _problems/test_pocket_unfavorite-28.R [ FAIL 13 | WARN 0 | SKIP 0 | PASS 45 ] ══ Failed tests ════════════════════════════════════════════════════════════════ ── Error ('test_pocket_add.R:32:7'): Valid case ──────────────────────────────── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test_pocket_add.R:32:7 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) ── Error ('test_pocket_archive.R:22:5'): pocket_archive - success generates message ── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test_pocket_archive.R:22:5 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) ── Error ('test_pocket_archive.R:38:5'): pocket_archive - one success, one error ── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test_pocket_archive.R:38:5 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) ── Error ('test_pocket_delete.R:23:5'): pocket_delete - success generates message ── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test_pocket_delete.R:23:5 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) ── Error ('test_pocket_delete.R:38:5'): pocket_delete - two successes ────────── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test_pocket_delete.R:38:5 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) ── Error ('test_pocket_delete.R:52:5'): pocket_delete - one success, one error ── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test_pocket_delete.R:52:5 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) ── Error ('test_pocket_favorite.R:22:5'): pocket_favorite - success generates message ── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test_pocket_favorite.R:22:5 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) ── Error ('test_pocket_tag.R:32:5'): pocket_tag tags_add - two successes ─────── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test_pocket_tag.R:32:5 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) ── Error ('test_pocket_tag.R:46:5'): pocket_tag tags_remove - two successes ──── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test_pocket_tag.R:46:5 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) ── Error ('test_pocket_tag.R:66:5'): pocket_tag tags_clear - two successes ───── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test_pocket_tag.R:66:5 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) ── Error ('test_pocket_tag.R:86:5'): pocket_tag tags_replace - two successes ─── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test_pocket_tag.R:86:5 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) ── Error ('test_pocket_tag.R:107:5'): pocket_tag tag_delete - success ────────── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test_pocket_tag.R:107:5 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) ── Error ('test_pocket_unfavorite.R:22:5'): pocket_unfavorite - success generates message ── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test_pocket_unfavorite.R:22:5 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) [ FAIL 13 | WARN 0 | SKIP 0 | PASS 45 ] Error: ! Test failures. Execution halted Package: pointblank Check: tests New result: ERROR Running ‘testthat.R’ [55s/55s] Running the tests in ‘tests/testthat.R’ failed. Complete output: > library(testthat) > library(pointblank) > library(dittodb) Loading required package: DBI > test_check("pointblank") Saving _problems/test-tidyselect_integration-76.R [ FAIL 1 | WARN 0 | SKIP 0 | PASS 1824 ] ══ Failed tests ════════════════════════════════════════════════════════════════ ── Failure ('test-tidyselect_integration.R:73:3'): Full range of tidyselect features available in column selection ── Expected exactly one success and no failures. Actually succeeded 2 times [ FAIL 1 | WARN 0 | SKIP 0 | PASS 1824 ] Error: ! Test failures. Execution halted Package: projmgr Check: tests New result: ERROR Running ‘testthat.R’ [5s/5s] Running the tests in ‘tests/testthat.R’ failed. Complete output: > library(testthat) > library(projmgr) > > test_check("projmgr") Saving _problems/test-get-engine-20.R Saving _problems/test-get-engine-30.R Saving _problems/test-get-engine-39.R Saving _problems/test-get-engine-47.R [ FAIL 4 | WARN 1 | SKIP 3 | PASS 106 ] ══ Skipped tests (3) ═══════════════════════════════════════════════════════════ • On CRAN (1): 'test-browse.R:3:1' • This set of tests should only be run manually (2): 'test-get-interactive.R:3:1', 'test-post-issue-interactive.R:3:1' ══ Failed tests ════════════════════════════════════════════════════════════════ ── Error ('test-get-engine.R:12:1'): Single item results are wrapped as list of lists ── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) ── Error ('test-get-engine.R:22:1'): Empty results are wrapped as empty character ── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) ── Error ('test-get-engine.R:33:1'): Repo metadata is added for non-empty results ── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) ── Error ('test-get-engine.R:41:1'): Repo metadata is not added for non-empty results ── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) [ FAIL 4 | WARN 1 | SKIP 3 | PASS 106 ] Error: ! Test failures. Execution halted Package: pyinit Check: tests New result: ERROR Running ‘debug.R’ [0s/0s] Running ‘testthat.R’ [1s/1s] Running the tests in ‘tests/testthat.R’ failed. Complete output: > if (require(testthat)) { + library(pyinit) + test_check("pyinit") + } else { + warning("'pyinit' requires 'testthat' for tests.") + } Loading required package: testthat Saving _problems/test-exact_fit-41.R [ FAIL 1 | WARN 0 | SKIP 0 | PASS 3 ] ══ Failed tests ════════════════════════════════════════════════════════════════ ── Error ('test-exact_fit.R:41:5'): Rank-deficient problems ──────────────────── Error in `expect_known_hash(round(res$coefficients[, obj_order], 4), "30f3b173bb32999ace3f3072ed")`: The package "digest" is required. Backtrace: ▆ 1. └─testthat::expect_known_hash(...) at test-exact_fit.R:41:5 2. └─rlang::check_installed("digest") [ FAIL 1 | WARN 0 | SKIP 0 | PASS 3 ] Error: ! Test failures. Execution halted Package: Quandl Check: tests New result: ERROR Running ‘testthat.R’ [3s/3s] Running the tests in ‘tests/testthat.R’ failed. Complete output: > library(testthat) > test_check("Quandl") Loading required package: Quandl Loading required package: xts Loading required package: zoo Attaching package: 'zoo' The following objects are masked from 'package:base': as.Date, as.Date.numeric Saving _problems/test-api-16.R Saving _problems/test-database-21.R Saving _problems/test-datatable-22.R Saving _problems/test-get-38.R Saving _problems/test-pointintime-22.R Saving _problems/test-search-18.R [ FAIL 6 | WARN 0 | SKIP 2 | PASS 4 ] ══ Skipped tests (2) ═══════════════════════════════════════════════════════════ • empty test (2): , ══ Failed tests ════════════════════════════════════════════════════════════════ ── Error ('test-api.r:6:1'): (code run outside of `test_that()`) ─────────────── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test-api.r:6:1 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) ── Error ('test-database.r:11:1'): (code run outside of `test_that()`) ───────── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test-database.r:11:1 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) ── Error ('test-datatable.r:8:1'): (code run outside of `test_that()`) ───────── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test-datatable.r:8:1 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) ── Error ('test-get.r:23:1'): (code run outside of `test_that()`) ────────────── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test-get.r:23:1 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) ── Error ('test-pointintime.r:8:1'): (code run outside of `test_that()`) ─────── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test-pointintime.r:8:1 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) ── Error ('test-search.r:4:1'): (code run outside of `test_that()`) ──────────── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test-search.r:4:1 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) [ FAIL 6 | WARN 0 | SKIP 2 | PASS 4 ] Error: ! Test failures. Execution halted Package: rags2ridges Check: tests New result: ERROR Running ‘testthat.R’ [96s/100s] Running the tests in ‘tests/testthat.R’ failed. Complete output: > library(testthat) > library(rags2ridges) > > test_check("rags2ridges") Saving _problems/test-armaRidgeP-66.R Saving _problems/test-armaRidgeP-66.R Saving _problems/test-armaRidgeP-66.R Saving _problems/test-armaRidgeP-66.R Saving _problems/test-armaRidgeP-66.R Saving _problems/test-armaRidgeP-66.R Saving _problems/test-armaRidgeP-66.R Saving _problems/test-armaRidgeP-66.R Saving _problems/test-armaRidgeP-66.R Saving _problems/test-armaRidgeP-66.R Saving _problems/test-armaRidgeP-66.R Saving _problems/test-armaRidgeP-66.R Saving _problems/test-armaRidgeP-66.R Saving _problems/test-armaRidgeP-66.R Saving _problems/test-armaRidgeP-66.R Saving _problems/test-armaRidgeP-66.R Saving _problems/test-armaRidgeP-66.R Saving _problems/test-armaRidgeP-66.R Saving _problems/test-armaRidgeP-66.R Saving _problems/test-armaRidgeP-66.R Saving _problems/test-armaRidgeP-66.R Saving _problems/test-armaRidgeP-66.R Saving _problems/test-armaRidgeP-66.R Saving 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_problems/test-xfcvl-26.R [ FAIL 830 | WARN 320 | SKIP 0 | PASS 984 ] ══ Failed tests ════════════════════════════════════════════════════════════════ ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-200 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-100 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-50 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-14 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-10 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1 ────────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+10 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+50 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+100 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+200 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+300 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = Inf ──────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = Inf ──────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-200 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-100 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-50 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-14 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-10 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1 ────────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+10 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+50 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+100 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+200 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+300 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = Inf ──────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = Inf ──────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-200 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-100 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-50 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-14 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-10 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1 ────────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+10 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+50 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+100 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+200 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+300 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = Inf ──────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = Inf ──────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-200 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-100 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-50 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-14 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-10 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1 ────────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+10 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+50 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+100 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+200 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+300 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = Inf ──────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = Inf ──────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-200 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-100 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-50 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-14 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-10 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1 ────────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+10 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+50 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+100 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+200 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+300 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = Inf ──────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = Inf ──────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-14 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-10 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1 ────────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+10 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+50 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+100 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+200 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+300 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = Inf ──────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = Inf ──────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-200 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-100 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-50 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-14 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-10 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1 ────────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+10 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+50 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+100 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+200 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+300 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = Inf ──────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = Inf ──────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-200 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-100 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-50 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-14 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-10 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1 ────────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+10 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+50 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+100 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+200 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+300 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = Inf ──────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = Inf ──────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:97:7'): proper values for very small lambda, type = DAIE ── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(all(abs(aa) <= abs(bb)), is_true()) at test-armaRidgeP.R:97:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-200 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-100 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-50 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-14 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-10 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1 ────────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+10 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+50 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+100 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+200 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+300 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = Inf ──────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = Inf ──────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:97:7'): proper values for very small lambda, type = DIAES ── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(all(abs(aa) <= abs(bb)), is_true()) at test-armaRidgeP.R:97:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-200 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-100 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-50 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-14 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-10 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1 ────────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+10 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+50 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+100 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+200 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+300 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = Inf ──────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = Inf ──────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:97:7'): proper values for very small lambda, type = DUPV ── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(all(abs(aa) <= abs(bb)), is_true()) at test-armaRidgeP.R:97:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-200 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-100 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-50 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-14 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-10 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1 ────────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+10 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+50 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+100 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+200 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+300 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = Inf ──────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = Inf ──────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:97:7'): proper values for very small lambda, type = DAPV ── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(all(abs(aa) <= abs(bb)), is_true()) at test-armaRidgeP.R:97:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-200 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-100 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-50 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-14 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-10 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1 ────────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+10 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+50 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+100 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+200 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+300 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = Inf ──────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = Inf ──────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:97:7'): proper values for very small lambda, type = DCPV ── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(all(abs(aa) <= abs(bb)), is_true()) at test-armaRidgeP.R:97:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-14 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-10 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1 ────────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+10 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+50 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+100 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+200 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+300 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = Inf ──────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = Inf ──────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:97:7'): proper values for very small lambda, type = DEPV ── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(all(abs(aa) <= abs(bb)), is_true()) at test-armaRidgeP.R:97:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-200 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-100 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-50 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-14 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-10 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1 ────────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+10 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+50 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+100 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+200 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+300 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = Inf ──────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = Inf ──────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:97:7'): proper values for very small lambda, type = Null ── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(all(abs(aa) <= abs(bb)), is_true()) at test-armaRidgeP.R:97:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-200 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-100 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-50 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-14 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-10 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1 ────────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+10 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+50 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+100 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+200 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+300 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = Inf ──────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = Inf ──────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:97:7'): proper values for very small lambda, type = DAIE ── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(all(abs(aa) <= abs(bb)), is_true()) at test-armaRidgeP.R:97:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-200 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-100 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-50 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-14 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-10 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1 ────────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+10 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+50 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+100 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+200 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+300 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = Inf ──────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = Inf ──────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:97:7'): proper values for very small lambda, type = DIAES ── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(all(abs(aa) <= abs(bb)), is_true()) at test-armaRidgeP.R:97:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-200 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-100 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-50 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-14 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-10 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1 ────────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+10 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+50 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+100 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+200 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+300 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = Inf ──────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = Inf ──────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:97:7'): proper values for very small lambda, type = DUPV ── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(all(abs(aa) <= abs(bb)), is_true()) at test-armaRidgeP.R:97:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-200 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-100 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-50 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-14 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-10 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1 ────────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+10 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+50 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+100 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+200 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+300 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = Inf ──────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = Inf ──────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:97:7'): proper values for very small lambda, type = DAPV ── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(all(abs(aa) <= abs(bb)), is_true()) at test-armaRidgeP.R:97:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-200 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-100 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-50 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-14 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-10 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1 ────────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+10 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+50 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+100 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+200 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+300 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = Inf ──────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = Inf ──────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:97:7'): proper values for very small lambda, type = DCPV ── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(all(abs(aa) <= abs(bb)), is_true()) at test-armaRidgeP.R:97:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-14 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-10 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1 ────────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+10 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+50 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+100 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+200 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+300 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = Inf ──────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = Inf ──────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:97:7'): proper values for very small lambda, type = DEPV ── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(all(abs(aa) <= abs(bb)), is_true()) at test-armaRidgeP.R:97:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-200 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-100 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-50 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-14 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-10 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1 ────────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+10 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+50 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+100 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+200 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+300 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = Inf ──────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = Inf ──────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:97:7'): proper values for very small lambda, type = Null ── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(all(abs(aa) <= abs(bb)), is_true()) at test-armaRidgeP.R:97:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-200 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-100 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-50 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-14 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-10 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1 ────────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+10 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+50 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+100 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+200 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+300 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = Inf ──────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = Inf ──────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-200 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-100 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-50 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-14 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-10 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1 ────────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+10 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+50 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+100 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+200 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+300 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = Inf ──────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = Inf ──────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-200 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-100 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-50 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-14 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-10 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1 ────────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+10 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+50 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+100 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+200 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+300 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = Inf ──────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = Inf ──────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-200 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-100 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-50 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-14 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-10 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1 ────────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+10 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+50 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+100 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+200 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+300 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = Inf ──────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = Inf ──────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-200 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-100 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-50 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-14 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-10 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1 ────────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+10 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+50 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+100 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+200 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+300 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = Inf ──────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = Inf ──────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-14 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-10 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1 ────────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+10 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+50 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+100 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+200 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+300 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = Inf ──────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = Inf ──────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-200 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-100 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-50 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-14 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-10 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1 ────────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+10 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+50 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+100 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+200 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+300 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = Inf ──────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = Inf ──────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-200 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-100 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-50 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-14 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-10 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1 ────────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+10 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+50 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+100 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+200 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+300 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = Inf ──────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = Inf ──────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:97:7'): proper values for very small lambda, type = DAIE ── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(all(abs(aa) <= abs(bb)), is_true()) at test-armaRidgeP.R:97:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-200 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-100 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-50 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-14 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-10 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1 ────────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+10 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+50 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+100 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+200 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+300 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = Inf ──────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = Inf ──────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:97:7'): proper values for very small lambda, type = DIAES ── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(all(abs(aa) <= abs(bb)), is_true()) at test-armaRidgeP.R:97:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-200 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-100 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-50 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-14 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-10 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1 ────────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+10 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+50 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+100 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+200 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+300 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = Inf ──────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = Inf ──────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:97:7'): proper values for very small lambda, type = DUPV ── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(all(abs(aa) <= abs(bb)), is_true()) at test-armaRidgeP.R:97:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-200 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-100 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-50 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-14 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-10 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1 ────────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+10 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+50 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+100 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+200 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+300 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = Inf ──────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = Inf ──────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:97:7'): proper values for very small lambda, type = DAPV ── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(all(abs(aa) <= abs(bb)), is_true()) at test-armaRidgeP.R:97:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-200 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-100 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-50 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-14 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-10 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1 ────────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+10 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+50 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+100 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+200 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+300 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = Inf ──────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = Inf ──────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:97:7'): proper values for very small lambda, type = DCPV ── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(all(abs(aa) <= abs(bb)), is_true()) at test-armaRidgeP.R:97:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-14 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-10 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1 ────────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+10 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+50 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+100 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+200 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+300 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = Inf ──────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = Inf ──────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:97:7'): proper values for very small lambda, type = DEPV ── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(all(abs(aa) <= abs(bb)), is_true()) at test-armaRidgeP.R:97:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-200 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-100 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-50 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-14 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-10 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1 ────────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+10 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+50 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+100 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+200 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+300 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = Inf ──────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = Inf ──────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:97:7'): proper values for very small lambda, type = Null ── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(all(abs(aa) <= abs(bb)), is_true()) at test-armaRidgeP.R:97:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-200 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-100 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-50 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-14 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-10 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1 ────────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+10 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+50 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+100 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+200 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+300 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = Inf ──────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = Inf ──────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:97:7'): proper values for very small lambda, type = DAIE ── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(all(abs(aa) <= abs(bb)), is_true()) at test-armaRidgeP.R:97:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-200 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-100 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-50 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-14 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-10 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1 ────────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+10 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+50 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+100 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+200 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+300 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = Inf ──────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = Inf ──────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:97:7'): proper values for very small lambda, type = DIAES ── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(all(abs(aa) <= abs(bb)), is_true()) at test-armaRidgeP.R:97:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-200 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-100 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-50 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-14 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-10 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1 ────────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+10 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+50 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+100 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+200 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+300 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = Inf ──────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = Inf ──────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:97:7'): proper values for very small lambda, type = DUPV ── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(all(abs(aa) <= abs(bb)), is_true()) at test-armaRidgeP.R:97:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-200 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-100 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-50 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-14 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-10 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1 ────────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+10 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+50 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+100 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+200 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+300 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = Inf ──────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = Inf ──────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:97:7'): proper values for very small lambda, type = DAPV ── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(all(abs(aa) <= abs(bb)), is_true()) at test-armaRidgeP.R:97:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-200 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-100 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-50 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-14 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-10 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1 ────────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+10 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+50 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+100 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+200 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+300 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = Inf ──────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = Inf ──────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:97:7'): proper values for very small lambda, type = DCPV ── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(all(abs(aa) <= abs(bb)), is_true()) at test-armaRidgeP.R:97:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-14 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-10 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1 ────────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+10 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+50 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+100 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+200 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+300 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = Inf ──────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = Inf ──────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:97:7'): proper values for very small lambda, type = DEPV ── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(all(abs(aa) <= abs(bb)), is_true()) at test-armaRidgeP.R:97:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-200 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-100 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-50 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-14 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-10 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1 ────────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+10 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+50 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+100 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+200 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+300 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = Inf ──────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = Inf ──────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:97:7'): proper values for very small lambda, type = Null ── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(all(abs(aa) <= abs(bb)), is_true()) at test-armaRidgeP.R:97:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-200 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-100 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-50 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-14 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-10 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1 ────────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+10 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+50 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+100 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+200 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+300 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = Inf ──────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = Inf ──────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-200 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-100 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-50 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-14 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-10 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1 ────────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+10 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+50 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+100 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+200 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+300 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = Inf ──────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = Inf ──────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-200 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-100 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-50 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-14 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-10 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1 ────────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+10 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+50 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+100 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+200 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+300 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = Inf ──────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = Inf ──────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-200 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-100 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-50 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-14 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-10 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1 ────────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+10 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+50 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+100 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+200 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+300 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = Inf ──────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = Inf ──────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-200 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-100 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-50 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-14 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-10 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1 ────────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+10 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+50 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+100 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+200 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+300 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = Inf ──────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = Inf ──────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-14 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-10 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1 ────────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+10 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+50 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+100 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+200 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+300 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = Inf ──────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = Inf ──────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-200 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-100 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-50 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-14 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-10 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1 ────────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+10 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+50 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+100 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+200 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+300 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = Inf ──────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = Inf ──────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-200 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-100 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-50 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-14 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-10 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1 ────────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+10 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+50 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+100 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+200 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+300 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = Inf ──────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = Inf ──────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-200 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-100 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-50 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-14 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-10 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1 ────────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+10 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+50 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+100 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+200 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+300 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = Inf ──────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = Inf ──────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-200 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-100 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-50 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-14 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-10 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1 ────────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+10 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+50 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+100 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+200 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+300 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = Inf ──────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = Inf ──────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-200 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-100 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-50 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-14 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-10 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1 ────────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+10 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+50 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+100 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+200 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+300 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = Inf ──────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = Inf ──────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-200 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-100 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-50 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-14 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-10 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1 ────────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+10 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+50 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+100 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+200 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+300 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = Inf ──────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = Inf ──────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-14 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-10 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1 ────────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+10 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+50 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+100 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+200 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+300 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = Inf ──────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = Inf ──────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-200 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-100 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-50 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-14 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-10 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1 ────────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+10 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+50 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+100 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+200 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+300 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = Inf ──────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = Inf ──────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-200 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-100 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-50 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-14 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-10 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1 ────────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+10 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+50 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+100 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+200 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+300 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = Inf ──────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = Inf ──────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:97:7'): proper values for very small lambda, type = DAIE ── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(all(abs(aa) <= abs(bb)), is_true()) at test-armaRidgeP.R:97:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-200 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-100 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-50 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-14 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-10 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1 ────────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+10 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+50 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+100 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+200 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+300 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = Inf ──────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = Inf ──────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:97:7'): proper values for very small lambda, type = DIAES ── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(all(abs(aa) <= abs(bb)), is_true()) at test-armaRidgeP.R:97:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-200 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-100 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-50 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-14 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-10 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1 ────────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+10 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+50 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+100 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+200 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+300 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = Inf ──────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = Inf ──────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:97:7'): proper values for very small lambda, type = DUPV ── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(all(abs(aa) <= abs(bb)), is_true()) at test-armaRidgeP.R:97:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-200 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-100 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-50 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-14 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-10 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1 ────────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+10 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+50 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+100 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+200 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+300 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = Inf ──────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = Inf ──────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:97:7'): proper values for very small lambda, type = DAPV ── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(all(abs(aa) <= abs(bb)), is_true()) at test-armaRidgeP.R:97:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-200 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-100 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-50 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-14 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-10 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1 ────────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+10 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+50 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+100 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+200 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+300 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = Inf ──────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = Inf ──────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:97:7'): proper values for very small lambda, type = DCPV ── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(all(abs(aa) <= abs(bb)), is_true()) at test-armaRidgeP.R:97:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-14 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-10 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1 ────────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+10 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+50 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+100 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+200 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+300 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = Inf ──────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = Inf ──────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:97:7'): proper values for very small lambda, type = DEPV ── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(all(abs(aa) <= abs(bb)), is_true()) at test-armaRidgeP.R:97:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-200 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-100 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-50 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-14 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e-10 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1 ────────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+10 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+50 ────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+100 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+200 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = 1e+300 ───────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = Inf ──────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:66:7'): proper format for lambda = Inf ──────────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(is.double(res), is_true()) at test-armaRidgeP.R:66:7 ── Error ('test-armaRidgeP.R:97:7'): proper values for very small lambda, type = Null ── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(all(abs(aa) <= abs(bb)), is_true()) at test-armaRidgeP.R:97:7 ── Error ('test-armaRidgeP.R:120:3'): Test armaRidgeP in various special cases (by reference) ── Error in `is_false()`: could not find function "is_false" Backtrace: ▆ 1. └─testthat::expect_that(...) at test-armaRidgeP.R:120:3 ── Error ('test-isSymmetricPD.R:29:3'): isSymmetricPD works as intended ──────── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(isSymmetricPD(pdS), is_true()) at test-isSymmetricPD.R:29:3 ── Error ('test-xfcvl.R:26:3'): .xfcl functions works properly on degenerated data ── Error in `is_true()`: could not find function "is_true" Backtrace: ▆ 1. └─testthat::expect_that(TRUE, is_true()) at test-xfcvl.R:26:3 [ FAIL 830 | WARN 320 | SKIP 0 | PASS 984 ] Error: ! Test failures. Execution halted Package: reactable Check: tests New result: ERROR Running ‘testthat.R’ [9s/9s] Running the tests in ‘tests/testthat.R’ failed. Complete output: > library(testthat) > library(reactable) > > test_check("reactable") Saving _problems/test-reactable-1514.R [ FAIL 1 | WARN 0 | SKIP 1 | PASS 743 ] ══ Skipped tests (1) ═══════════════════════════════════════════════════════════ • On CRAN (1): 'test-reactable.R:1346:1' ══ Failed tests ════════════════════════════════════════════════════════════════ ── Failure ('test-reactable.R:1514:3'): static rendering ─────────────────────── Expected `grepl(">₹1,234,567.40<", html, fixed = TRUE)` to be TRUE. Differences: `actual`: FALSE `expected`: TRUE [ FAIL 1 | WARN 0 | SKIP 1 | PASS 743 ] Error: ! Test failures. Execution halted Package: regmedint Check: tests New result: ERROR Running ‘testthat.R’ [37s/37s] Running the tests in ‘tests/testthat.R’ failed. Complete output: > library(testthat) > library(regmedint) > > test_check("regmedint") Saving _problems/test-05_calc_myreg-133.R Saving _problems/test-05_calc_myreg-174.R Saving _problems/test-05_calc_myreg-215.R Saving _problems/test-05_calc_myreg-256.R [ FAIL 4 | WARN 0 | SKIP 2 | PASS 4128 ] ══ Skipped tests (2) ═══════════════════════════════════════════════════════════ • empty test (2): 'test-01_regmedint_class_ui.R:402:9', 'test-01_regmedint_class_ui.R:421:9' ══ Failed tests ════════════════════════════════════════════════════════════════ ── Error ('test-05_calc_myreg.R:112:9'): calc_myreg / calls calc_myreg_mreg_linear_yreg_linear when mreg linear / yreg linear ── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test-05_calc_myreg.R:112:9 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) ── Error ('test-05_calc_myreg.R:153:9'): calc_myreg / calls calc_myreg_mreg_linear_yreg_logistic when mreg linear / yreg logistic ── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test-05_calc_myreg.R:153:9 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) ── Error ('test-05_calc_myreg.R:194:9'): calc_myreg / calls calc_myreg_mreg_logistic_yreg_linear when mreg logistic / yreg linear ── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test-05_calc_myreg.R:194:9 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) ── Error ('test-05_calc_myreg.R:235:9'): calc_myreg / calls calc_myreg_mreg_logistic_yreg_logistic when mreg logistic / yreg logistic ── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test-05_calc_myreg.R:235:9 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) [ FAIL 4 | WARN 0 | SKIP 2 | PASS 4128 ] Error: ! Test failures. Execution halted Package: Rexperigen Check: tests New result: ERROR Running ‘testthat.R’ [3s/3s] Running the tests in ‘tests/testthat.R’ failed. Complete output: > library(testthat) > library(Rexperigen) > > test_check("Rexperigen") Saving _problems/test-download-19.R Saving _problems/test-download-34.R Saving _problems/test-download-51.R Saving _problems/test-download-65.R Saving _problems/test-download-78.R Saving _problems/test-registration-29.R Saving _problems/test-registration-43.R Saving _problems/test-registration-56.R Saving _problems/test-server-10.R Saving _problems/test-server-47.R Saving _problems/test-server-88.R Saving _problems/test-utils-21.R Saving _problems/test-utils-52.R Saving _problems/test-utils-67.R Saving _problems/test-utils-114.R Saving _problems/test-zzz-7.R Saving _problems/test-zzz-8.R [ FAIL 17 | WARN 0 | SKIP 0 | PASS 23 ] ══ Failed tests ════════════════════════════════════════════════════════════════ ── Error ('test-download.R:12:5'): makecsv with no auth, server 1 ────────────── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test-download.R:12:5 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) ── Error ('test-download.R:27:5'): makecsv with no auth, server 2 ────────────── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test-download.R:27:5 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) ── Error ('test-download.R:42:5'): makecsv with auth, server 2 ───────────────── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test-download.R:42:5 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) ── Error ('test-download.R:58:5'): getDestinations ───────────────────────────── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test-download.R:58:5 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) ── Error ('test-download.R:71:5'): getUsers ──────────────────────────────────── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test-download.R:71:5 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) ── Error ('test-registration.R:21:5'): registering new experiment ────────────── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test-registration.R:21:5 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) ── Error ('test-registration.R:35:5'): removing registration ─────────────────── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test-registration.R:35:5 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) ── Error ('test-registration.R:48:5'): listing registered experiments ────────── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test-registration.R:48:5 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) ── Error ('test-server.R:4:5'): setExperigenServer ───────────────────────────── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test-server.R:4:5 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) ── Error ('test-server.R:33:5'): Setting credentials ─────────────────────────── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test-server.R:33:5 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) ── Error ('test-server.R:74:5'): creating experimenters ──────────────────────── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test-server.R:74:5 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) ── Error ('test-utils.R:15:5'): API requests ─────────────────────────────────── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test-utils.R:15:5 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) ── Error ('test-utils.R:38:5'): Authed API request ───────────────────────────── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test-utils.R:38:5 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) ── Error ('test-utils.R:58:5'): serverVersion ────────────────────────────────── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test-utils.R:58:5 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) ── Error ('test-utils.R:108:5'): cleanurl ────────────────────────────────────── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test-utils.R:108:5 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) ── Failure ('test-zzz.R:7:3'): initialization is okay ────────────────────────── Expected `getOption("Rexperigen.experimenter")` to equal "". Differences: 1/1 mismatches x[1]: "alma" y[1]: "" ── Failure ('test-zzz.R:8:3'): initialization is okay ────────────────────────── Expected `getOption("Rexperigen.password")` to equal "". Differences: 1/1 mismatches x[1]: "korte" y[1]: "" [ FAIL 17 | WARN 0 | SKIP 0 | PASS 23 ] Error: ! Test failures. Execution halted Package: rmdl Check: tests New result: ERROR Running ‘testthat.R’ [14s/14s] Running the tests in ‘tests/testthat.R’ failed. Complete output: > library(testthat) > library(rmdl) Loading required package: vctrs Loading required package: tibble Attaching package: 'tibble' The following object is masked from 'package:vctrs': data_frame Attaching package: 'rmdl' The following object is masked from 'package:testthat': describe > > test_check("rmdl") Interaction term `green` was applied to exposure term `witch` Interaction term `green` was applied to exposure term `witch` Interaction term `west` was applied to exposure term `witch` Using `fundamental` decomposition pattern: - Mediation term: NA - Stratifying term: NA Using `fundamental` decomposition pattern: - Mediation term: NA - Stratifying term: NA Interaction term `green` was applied to exposure term `witch` Saving _problems/test-terms-213.R [ FAIL 1 | WARN 0 | SKIP 4 | PASS 206 ] ══ Skipped tests (4) ═══════════════════════════════════════════════════════════ • Skipping (1): 'test-formulas.R:57:2' • empty test (3): 'test-interaction.R:41:1', 'test-model-table.R:272:1', 'test-patterns.R:36:1' ══ Failed tests ════════════════════════════════════════════════════════════════ ── Error ('test-terms.R:213:2'): interaction terms are appropriately made ────── Error in `expect(length(t), 5)`: `ok` must be `TRUE` or `FALSE`, not the number 5. Backtrace: ▆ 1. └─testthat::expect(ok = length(t)) at test-terms.R:213:9 2. └─testthat:::check_bool(ok) 3. └─testthat:::stop_input_type(...) 4. └─rlang::abort(message, ..., call = call, arg = arg) [ FAIL 1 | WARN 0 | SKIP 4 | PASS 206 ] Error: ! Test failures. Execution halted Package: rosetteApi Check: tests New result: ERROR Running ‘testthat.R’ [1s/1s] Running the tests in ‘tests/testthat.R’ failed. Complete output: > library(testthat) > library(rosetteApi) > > test_check("rosetteApi") Saving _problems/test_api-8.R Saving _problems/test_api-19.R [ FAIL 2 | WARN 0 | SKIP 0 | PASS 13 ] ══ Failed tests ════════════════════════════════════════════════════════════════ ── Error ('test_api.R:3:3'): httr::GET function mocks correctly ──────────────── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test_api.R:3:3 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) ── Error ('test_api.R:14:3'): httr::POST functions mock correctly ────────────── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test_api.R:14:3 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) [ FAIL 2 | WARN 0 | SKIP 0 | PASS 13 ] Error: ! Test failures. Execution halted Package: Rpolyhedra Check: tests New result: ERROR Running ‘testthat.R’ [15s/15s] Running the tests in ‘tests/testthat.R’ failed. Complete output: > library(Rpolyhedra) > library(stringr) > library(lgr) > library(rgl) > library(geometry) > library(testthat) > > # Change threshold to ERROR. Comment out/change if verbosity required for development > lgr::basic_config(threshold = "error") [error] root appenders: console: [all] -> console > #' getDataDirMockedTest mocked function for a temp dest folder for testing proposes > > > > testthat::test_check("Rpolyhedra") Saving _problems/test_package_lib-41.R [ FAIL 1 | WARN 0 | SKIP 0 | PASS 646 ] ══ Failed tests ════════════════════════════════════════════════════════════════ ── Error ('test_package_lib.R:22:3'): test on package lib functions ──────────── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test_package_lib.R:22:3 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) [ FAIL 1 | WARN 0 | SKIP 0 | PASS 646 ] Error: ! Test failures. Execution halted Package: RPresto Check: tests New result: ERROR Running ‘testthat.R’ [10s/10s] Running the tests in ‘tests/testthat.R’ failed. Complete output: > # Copyright (c) Meta Platforms, Inc. and affiliates. > # All rights reserved. > # > # This source code is licensed under the BSD-style license found in the > # LICENSE file in the root directory of this source tree. > > library("testthat") > library("DBI") > library("dplyr") Attaching package: 'dplyr' The following objects are masked from 'package:stats': filter, lag The following objects are masked from 'package:base': intersect, setdiff, setequal, union > library("RPresto") > > test_check("RPresto") Warning: Presto connection default is not available Saving _problems/test-PrestoQuery-10.R Saving _problems/test-PrestoQuery-57.R Saving _problems/test-dbClearResult-16.R Saving _problems/test-dbConnect-145.R Saving _problems/test-dbDisconnect-15.R Saving _problems/test-dbFetch-33.R Saving _problems/test-dbFetch-217.R Saving _problems/test-dbFetch-263.R Saving _problems/test-dbGetInfo-35.R Saving _problems/test-dbGetQuery-40.R Saving _problems/test-dbGetQuery-100.R Saving _problems/test-dbGetQuery-143.R Saving _problems/test-dbGetRowCount-24.R Saving _problems/test-dbGetStatement-19.R Saving _problems/test-dbHasCompleted-19.R Saving _problems/test-dbIsValid-46.R Saving _problems/test-dbIsValid-86.R Saving _problems/test-dbIsValid-204.R Saving _problems/test-dbListFields-46.R Saving _problems/test-dbListFields-98.R Saving _problems/test-dbListFields-248.R Saving _problems/test-dbListFields-294.R Saving _problems/test-dbListTables-21.R Saving _problems/test-dbSendQuery-54.R Saving _problems/test-dbSendQuery-96.R Saving _problems/test-dbSendQuery-129.R Saving _problems/test-dbSendQuery-147.R Saving _problems/test-dbSendQuery-164.R Saving _problems/test-dbSendQuery-184.R Saving _problems/test-dbSendQuery-206.R Saving _problems/test-db_explain-22.R Saving _problems/test-db_query_fields-39.R Saving _problems/test-fetch-26.R Saving _problems/test-sql_escape_date-16.R Saving _problems/test-sql_escape_datetime-16.R Saving _problems/test-translate_sql-14.R Saving _problems/test-translate_sql-122.R Saving _problems/test-translate_sql-161.R Saving _problems/test-translate_sql-327.R Saving _problems/test-translate_sql-348.R Saving _problems/test-translate_sql-400.R Saving _problems/test-translate_sql-428.R [ FAIL 42 | WARN 0 | SKIP 68 | PASS 53 ] ══ Skipped tests (68) ══════════════════════════════════════════════════════════ • On CRAN (68): 'test-bigint_handling.R:10:3', 'test-bigint_handling.R:23:3', 'test-bigint_handling.R:52:3', 'test-copy_to.src_presto.R:67:3', 'test-copy_to.src_presto.R:75:3', 'test-copy_to.src_presto.R:81:3', 'test-cte.R:10:3', 'test-cte.R:26:3', 'test-cte.R:67:3', 'test-cte.R:108:3', 'test-cte.R:204:3', 'test-data_types.R:10:3', 'test-data_types.R:31:3', 'test-data_types.R:67:3', 'test-data_types.R:98:3', 'test-data_types.R:128:3', 'test-dbAppendTable.R:10:3', 'test-dbClearResult.R:10:3', 'test-dbCreateTable.R:10:3', 'test-dbCreateTable.R:41:3', 'test-dbCreateTableAs.R:10:3', 'test-dbCreateTableAs.R:54:3', 'test-dbDisconnect.R:10:3', 'test-dbExecute.R:10:3', 'test-dbExecute.R:33:3', 'test-dbExecute.R:56:3', 'test-dbExecute.R:79:3', 'test-dbExistsTable.R:10:3', 'test-dbFetch.R:10:3', 'test-dbGetInfo.R:10:3', 'test-dbGetQuery.R:10:3', 'test-dbGetRowCount.R:10:3', 'test-dbGetStatement.R:10:3', 'test-dbHasCompleted.R:10:3', 'test-dbIsValid.R:10:3', 'test-dbListFields.R:10:3', 'test-dbListFields.R:30:3', 'test-dbListTables.R:10:3', 'test-dbReadTable.R:10:3', 'test-dbRemoveTable.R:10:3', 'test-dbRenameTable.R:30:3', 'test-dbSendQuery.R:10:3', 'test-dbWriteTable.R:10:3', 'test-dbWriteTable.R:76:3', 'test-db_explain.R:10:3', 'test-db_query_fields.R:10:3', 'test-dbplyr-sql.R:10:3', 'test-dplyr.integration.R:10:3', 'test-dplyr_as_type.R:10:3', 'test-extra.credential.R:10:3', 'test-fetch.R:10:3', 'test-integration.R:31:3', 'test-presto_field.R:26:3', 'test-presto_field.R:220:3', 'test-presto_field.R:516:3', 'test-presto_field.R:742:3', 'test-presto_field.R:806:3', 'test-presto_field.R:840:3', 'test-presto_field.R:898:3', 'test-presto_field.R:916:3', 'test-presto_field.R:932:3', 'test-presto_field.R:940:3', 'test-session.property.R:19:3', 'test-session.timezone.R:21:3', 'test-tbl.src_presto.R:10:3', 'test-tbl.src_presto.R:44:3', 'test-tbl.src_presto.R:55:3', 'test-translate_sql.R:198:3' ══ Failed tests ════════════════════════════════════════════════════════════════ ── Error ('test-PrestoQuery.R:10:3'): PrestoQuery methods work correctly ─────── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─RPresto:::setup_mock_connection() at test-PrestoQuery.R:10:3 2. └─testthat::with_mock(...) at ./helper-mock_connection.R:8:3 3. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 4. └─lifecycle:::deprecate_stop0(msg) 5. └─rlang::cnd_signal(...) ── Error ('test-PrestoQuery.R:57:3'): PrestoQuery methods work correctly with POST data ── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─RPresto:::setup_mock_connection() at test-PrestoQuery.R:57:3 2. └─testthat::with_mock(...) at ./helper-mock_connection.R:8:3 3. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 4. └─lifecycle:::deprecate_stop0(msg) 5. └─rlang::cnd_signal(...) ── Error ('test-dbClearResult.R:16:3'): dbClearResult works with mock ────────── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─RPresto:::setup_mock_connection() at test-dbClearResult.R:16:3 2. └─testthat::with_mock(...) at ./helper-mock_connection.R:8:3 3. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 4. └─lifecycle:::deprecate_stop0(msg) 5. └─rlang::cnd_signal(...) ── Error ('test-dbConnect.R:10:3'): dbConnect constructs PrestoConnection correctly ── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test-dbConnect.R:10:3 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) ── Error ('test-dbDisconnect.R:15:3'): dbDisconnect works with mock ──────────── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─RPresto:::setup_mock_connection() at test-dbDisconnect.R:15:3 2. └─testthat::with_mock(...) at ./helper-mock_connection.R:8:3 3. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 4. └─lifecycle:::deprecate_stop0(msg) 5. └─rlang::cnd_signal(...) ── Error ('test-dbFetch.R:33:3'): dbFetch works with mock ────────────────────── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─RPresto:::setup_mock_connection() at test-dbFetch.R:33:3 2. └─testthat::with_mock(...) at ./helper-mock_connection.R:8:3 3. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 4. └─lifecycle:::deprecate_stop0(msg) 5. └─rlang::cnd_signal(...) ── Error ('test-dbFetch.R:217:3'): dbFetch rbind works correctly ─────────────── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. i Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─RPresto:::setup_mock_connection() at test-dbFetch.R:217:3 2. └─testthat::with_mock(...) at ./helper-mock_connection.R:8:3 3. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 4. └─lifecycle:::deprecate_stop0(msg) 5. └─rlang::cnd_signal(...) ── Error ('test-dbFetch.R:263:3'): dbFetch rbind works with zero row chunks ──── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. i Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─RPresto:::setup_mock_connection() at test-dbFetch.R:263:3 2. └─testthat::with_mock(...) at ./helper-mock_connection.R:8:3 3. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 4. └─lifecycle:::deprecate_stop0(msg) 5. └─rlang::cnd_signal(...) ── Error ('test-dbGetInfo.R:35:3'): dbGetInfo works with mock ────────────────── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─RPresto:::setup_mock_connection() at test-dbGetInfo.R:35:3 2. └─testthat::with_mock(...) at ./helper-mock_connection.R:8:3 3. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 4. └─lifecycle:::deprecate_stop0(msg) 5. └─rlang::cnd_signal(...) ── Error ('test-dbGetQuery.R:40:3'): dbGetQuery works with mock ──────────────── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. i Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─RPresto:::setup_mock_connection() at test-dbGetQuery.R:40:3 2. └─testthat::with_mock(...) at ./helper-mock_connection.R:8:3 3. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 4. └─lifecycle:::deprecate_stop0(msg) 5. └─rlang::cnd_signal(...) ── Error ('test-dbGetQuery.R:100:3'): dbGetQuery works with data in POST response ── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─RPresto:::setup_mock_connection() at test-dbGetQuery.R:100:3 2. └─testthat::with_mock(...) at ./helper-mock_connection.R:8:3 3. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 4. └─lifecycle:::deprecate_stop0(msg) 5. └─rlang::cnd_signal(...) ── Error ('test-dbGetQuery.R:143:3'): Inconsistent data in chunks fail ───────── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─RPresto:::setup_mock_connection() at test-dbGetQuery.R:143:3 2. └─testthat::with_mock(...) at ./helper-mock_connection.R:8:3 3. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 4. └─lifecycle:::deprecate_stop0(msg) 5. └─rlang::cnd_signal(...) ── Error ('test-dbGetRowCount.R:24:3'): dbGetRowCount works with mock ────────── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─RPresto:::setup_mock_connection() at test-dbGetRowCount.R:24:3 2. └─testthat::with_mock(...) at ./helper-mock_connection.R:8:3 3. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 4. └─lifecycle:::deprecate_stop0(msg) 5. └─rlang::cnd_signal(...) ── Error ('test-dbGetStatement.R:19:3'): dbGetStatement works with mock ──────── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─RPresto:::setup_mock_connection() at test-dbGetStatement.R:19:3 2. └─testthat::with_mock(...) at ./helper-mock_connection.R:8:3 3. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 4. └─lifecycle:::deprecate_stop0(msg) 5. └─rlang::cnd_signal(...) ── Error ('test-dbHasCompleted.R:19:3'): dbHasCompleted works with mock ──────── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─RPresto:::setup_mock_connection() at test-dbHasCompleted.R:19:3 2. └─testthat::with_mock(...) at ./helper-mock_connection.R:8:3 3. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 4. └─lifecycle:::deprecate_stop0(msg) 5. └─rlang::cnd_signal(...) ── Error ('test-dbIsValid.R:46:3'): dbIsValid works with mock - successful queries ── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─RPresto:::setup_mock_connection() at test-dbIsValid.R:46:3 2. └─testthat::with_mock(...) at ./helper-mock_connection.R:8:3 3. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 4. └─lifecycle:::deprecate_stop0(msg) 5. └─rlang::cnd_signal(...) ── Error ('test-dbIsValid.R:86:3'): dbIsValid works with mock - retries and failures ── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─RPresto:::setup_mock_connection() at test-dbIsValid.R:86:3 2. └─testthat::with_mock(...) at ./helper-mock_connection.R:8:3 3. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 4. └─lifecycle:::deprecate_stop0(msg) 5. └─rlang::cnd_signal(...) ── Error ('test-dbIsValid.R:204:3'): dbIsValid works with mock - dbClearResult ── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─RPresto:::setup_mock_connection() at test-dbIsValid.R:204:3 2. └─testthat::with_mock(...) at ./helper-mock_connection.R:8:3 3. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 4. └─lifecycle:::deprecate_stop0(msg) 5. └─rlang::cnd_signal(...) ── Error ('test-dbListFields.R:46:3'): dbListFields works with mock - PrestoConnection ── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─RPresto:::setup_mock_connection() at test-dbListFields.R:46:3 2. └─testthat::with_mock(...) at ./helper-mock_connection.R:8:3 3. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 4. └─lifecycle:::deprecate_stop0(msg) 5. └─rlang::cnd_signal(...) ── Error ('test-dbListFields.R:98:3'): dbListFields works with mock - PrestoResult ── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─RPresto:::setup_mock_connection() at test-dbListFields.R:98:3 2. └─testthat::with_mock(...) at ./helper-mock_connection.R:8:3 3. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 4. └─lifecycle:::deprecate_stop0(msg) 5. └─rlang::cnd_signal(...) ── Error ('test-dbListFields.R:248:3'): dbListFields works with mock - PrestoResult - POST data ── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─RPresto:::setup_mock_connection() at test-dbListFields.R:248:3 2. └─testthat::with_mock(...) at ./helper-mock_connection.R:8:3 3. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 4. └─lifecycle:::deprecate_stop0(msg) 5. └─rlang::cnd_signal(...) ── Error ('test-dbListFields.R:294:3'): dbListFields works with mock - PrestoResult - POST columns ── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─RPresto:::setup_mock_connection() at test-dbListFields.R:294:3 2. └─testthat::with_mock(...) at ./helper-mock_connection.R:8:3 3. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 4. └─lifecycle:::deprecate_stop0(msg) 5. └─rlang::cnd_signal(...) ── Error ('test-dbListTables.R:21:3'): dbListTables works with mock ──────────── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─RPresto:::setup_mock_connection() at test-dbListTables.R:21:3 2. └─testthat::with_mock(...) at ./helper-mock_connection.R:8:3 3. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 4. └─lifecycle:::deprecate_stop0(msg) 5. └─rlang::cnd_signal(...) ── Error ('test-dbSendQuery.R:54:3'): dbSendQuery works with mock - status code 404 ── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─RPresto:::setup_mock_connection() at test-dbSendQuery.R:54:3 2. └─testthat::with_mock(...) at ./helper-mock_connection.R:8:3 3. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 4. └─lifecycle:::deprecate_stop0(msg) 5. └─rlang::cnd_signal(...) ── Error ('test-dbSendQuery.R:96:3'): dbSendQuery works with mock - status code 503 ── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─RPresto:::setup_mock_connection() at test-dbSendQuery.R:96:3 2. └─testthat::with_mock(...) at ./helper-mock_connection.R:8:3 3. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 4. └─lifecycle:::deprecate_stop0(msg) 5. └─rlang::cnd_signal(...) ── Error ('test-dbSendQuery.R:129:3'): dbSendQuery works with mock - status code 400 ── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─RPresto:::setup_mock_connection() at test-dbSendQuery.R:129:3 2. └─testthat::with_mock(...) at ./helper-mock_connection.R:8:3 3. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 4. └─lifecycle:::deprecate_stop0(msg) 5. └─rlang::cnd_signal(...) ── Error ('test-dbSendQuery.R:147:3'): dbSendQuery works with mock - status code 200, FAILED ── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─RPresto:::setup_mock_connection() at test-dbSendQuery.R:147:3 2. └─testthat::with_mock(...) at ./helper-mock_connection.R:8:3 3. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 4. └─lifecycle:::deprecate_stop0(msg) 5. └─rlang::cnd_signal(...) ── Error ('test-dbSendQuery.R:164:3'): dbSendQuery works with mock - status code 500 ── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─RPresto:::setup_mock_connection() at test-dbSendQuery.R:164:3 2. └─testthat::with_mock(...) at ./helper-mock_connection.R:8:3 3. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 4. └─lifecycle:::deprecate_stop0(msg) 5. └─rlang::cnd_signal(...) ── Error ('test-dbSendQuery.R:184:3'): dbSendQuery works with mock - regular ─── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─RPresto:::setup_mock_connection() at test-dbSendQuery.R:184:3 2. └─testthat::with_mock(...) at ./helper-mock_connection.R:8:3 3. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 4. └─lifecycle:::deprecate_stop0(msg) 5. └─rlang::cnd_signal(...) ── Error ('test-dbSendQuery.R:206:3'): dbSendQuery works with mock - POST data ── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─RPresto:::setup_mock_connection() at test-dbSendQuery.R:206:3 2. └─testthat::with_mock(...) at ./helper-mock_connection.R:8:3 3. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 4. └─lifecycle:::deprecate_stop0(msg) 5. └─rlang::cnd_signal(...) ── Error ('test-db_explain.R:22:3'): db_explain works with mock ──────────────── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─RPresto:::setup_mock_dplyr_connection() at test-db_explain.R:22:3 2. └─testthat::with_mock(...) at ./helper-mock_connection.R:38:3 3. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 4. └─lifecycle:::deprecate_stop0(msg) 5. └─rlang::cnd_signal(...) ── Error ('test-db_query_fields.R:39:3'): db_query_fields works with mock ────── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─RPresto:::setup_mock_dplyr_connection() at test-db_query_fields.R:39:3 2. └─testthat::with_mock(...) at ./helper-mock_connection.R:38:3 3. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 4. └─lifecycle:::deprecate_stop0(msg) 5. └─rlang::cnd_signal(...) ── Error ('test-fetch.R:26:3'): fetch works with mock ────────────────────────── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─RPresto:::setup_mock_connection() at test-fetch.R:26:3 2. └─testthat::with_mock(...) at ./helper-mock_connection.R:8:3 3. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 4. └─lifecycle:::deprecate_stop0(msg) 5. └─rlang::cnd_signal(...) ── Error ('test-sql_escape_date.R:16:3'): as() works ─────────────────────────── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. i Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─RPresto:::setup_mock_dplyr_connection() at test-sql_escape_date.R:16:3 2. └─testthat::with_mock(...) at ./helper-mock_connection.R:38:3 3. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 4. └─lifecycle:::deprecate_stop0(msg) 5. └─rlang::cnd_signal(...) ── Error ('test-sql_escape_datetime.R:16:3'): as() works ─────────────────────── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. i Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─RPresto:::setup_mock_dplyr_connection() at test-sql_escape_datetime.R:16:3 2. └─testthat::with_mock(...) at ./helper-mock_connection.R:38:3 3. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 4. └─lifecycle:::deprecate_stop0(msg) 5. └─rlang::cnd_signal(...) ── Error ('test-translate_sql.R:14:3'): as() works ───────────────────────────── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. i Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─RPresto:::setup_mock_dplyr_connection() at test-translate_sql.R:14:3 2. └─testthat::with_mock(...) at ./helper-mock_connection.R:38:3 3. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 4. └─lifecycle:::deprecate_stop0(msg) 5. └─rlang::cnd_signal(...) ── Error ('test-translate_sql.R:122:3'): as.() works ───────────────────── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. i Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─RPresto:::setup_mock_dplyr_connection() at test-translate_sql.R:122:3 2. └─testthat::with_mock(...) at ./helper-mock_connection.R:38:3 3. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 4. └─lifecycle:::deprecate_stop0(msg) 5. └─rlang::cnd_signal(...) ── Error ('test-translate_sql.R:161:3'): `[[` works for char/numeric indices ─── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. i Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─RPresto:::setup_mock_dplyr_connection() at test-translate_sql.R:161:3 2. └─testthat::with_mock(...) at ./helper-mock_connection.R:38:3 3. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 4. └─lifecycle:::deprecate_stop0(msg) 5. └─rlang::cnd_signal(...) ── Error ('test-translate_sql.R:327:3'): is.[in]finite() works ───────────────── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. i Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─RPresto:::setup_mock_dplyr_connection() at test-translate_sql.R:327:3 2. └─testthat::with_mock(...) at ./helper-mock_connection.R:38:3 3. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 4. └─lifecycle:::deprecate_stop0(msg) 5. └─rlang::cnd_signal(...) ── Error ('test-translate_sql.R:348:3'): quantile() and median() throw errors ── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. i Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─RPresto:::setup_mock_dplyr_connection() at test-translate_sql.R:348:3 2. └─testthat::with_mock(...) at ./helper-mock_connection.R:38:3 3. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 4. └─lifecycle:::deprecate_stop0(msg) 5. └─rlang::cnd_signal(...) ── Error ('test-translate_sql.R:400:3'): first(), last(), and nth() work ─────── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. i Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─RPresto:::setup_mock_dplyr_connection() at test-translate_sql.R:400:3 2. └─testthat::with_mock(...) at ./helper-mock_connection.R:38:3 3. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 4. └─lifecycle:::deprecate_stop0(msg) 5. └─rlang::cnd_signal(...) ── Error ('test-translate_sql.R:428:3'): paste() works ───────────────────────── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. i Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─RPresto:::setup_mock_dplyr_connection() at test-translate_sql.R:428:3 2. └─testthat::with_mock(...) at ./helper-mock_connection.R:38:3 3. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 4. └─lifecycle:::deprecate_stop0(msg) 5. └─rlang::cnd_signal(...) [ FAIL 42 | WARN 0 | SKIP 68 | PASS 53 ] Error: ! Test failures. Execution halted Package: rtables Check: tests New result: ERROR Running ‘testthat.R’ [244s/245s] Running the tests in ‘tests/testthat.R’ failed. Complete output: > if (requireNamespace("testthat", quietly = TRUE)) { + library(testthat) + library(rtables) + test_check("rtables", reporter = "check") + } Loading required package: formatters Attaching package: 'formatters' The following object is masked from 'package:base': %||% Loading required package: magrittr Attaching package: 'magrittr' The following objects are masked from 'package:testthat': equals, is_less_than, not Attaching package: 'rtables' The following object is masked from 'package:utils': str Attaching package: 'dplyr' The following objects are masked from 'package:stats': filter, lag The following objects are masked from 'package:base': intersect, setdiff, setequal, union Saving _problems/test-make-afun-135.R [ FAIL 1 | WARN 0 | SKIP 0 | PASS 1746 ] ══ Failed tests ════════════════════════════════════════════════════════════════ ── Error ('test-make-afun.R:135:3'): make_afun works for df functions ────────── Error in `expect(unlist(ares2$b), 6)`: `ok` must be `TRUE` or `FALSE`, not the number 6. Backtrace: ▆ 1. └─testthat::expect(ok = unlist(ares2$b)) at test-make-afun.R:135:3 2. └─testthat:::check_bool(ok) 3. └─testthat:::stop_input_type(...) 4. └─rlang::abort(message, ..., call = call, arg = arg) [ FAIL 1 | WARN 0 | SKIP 0 | PASS 1746 ] Error: ! Test failures. Execution halted Package: RTD Check: tests New result: ERROR Running ‘testthat.R’ [3s/4s] Running the tests in ‘tests/testthat.R’ failed. Complete output: > library(testthat) > library(mockery) > library(RTD) > > test_check("RTD") Saving _problems/test-td-24.R Saving _problems/test-td-45.R [ FAIL 2 | WARN 0 | SKIP 0 | PASS 10 ] ══ Failed tests ════════════════════════════════════════════════════════════════ ── Error ('test-td.R:12:3'): td_upload works with mock ───────────────────────── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test-td.R:12:3 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) ── Error ('test-td.R:32:3'): td_upload works with mock when the table already exists ── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test-td.R:32:3 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) [ FAIL 2 | WARN 0 | SKIP 0 | PASS 10 ] Error: ! Test failures. Execution halted Package: scorematchingad Check: tests New result: ERROR Running ‘testthat.R’ [152s/154s] Running the tests in ‘tests/testthat.R’ failed. Complete output: > library(testthat) > > test_check("scorematchingad") Loading required package: scorematchingad The Jacobian of the Hessian was non-zero for row 1 of xmat and dynmat Saving _problems/test-vMF-102.R Saving _problems/test-vMF-102.R [ FAIL 2 | WARN 0 | SKIP 18 | PASS 427 ] ══ Skipped tests (18) ══════════════════════════════════════════════════════════ • Fixing many of the elements doesn't improve smdbjgrad (1): 'test-FB.R:117:3' • Hardcoded and closed methods both fail on this. But not on the microbiome data (1): 'test-cppad_closed.R:60:3' • On CRAN (15): 'test-Bingham.R:76:3', 'test-Bingham.R:97:3', 'test-FB.R:48:3', 'test-FB.R:139:3', 'test-Windham-Microdata.R:1:1', 'test-Windham.R:2:3', 'test-closed-dirichlet.R:1:1', 'test-closed-ppi-Ralr.R:2:3', 'test-closed-ppi-sphere-simplex.R:171:3', 'test-gengamma_alr.R:25:3', 'test-ppi-difficultAL.R:1:1', 'test-ppi-microdata.R:9:1', 'test-ppi-microdata.R:64:1', 'test-ppi_mmmm.R:1:1', 'test-rppi.R:10:3' • Only for research. (1): 'test-FB.R:97:3' ══ Failed tests ════════════════════════════════════════════════════════════════ ── Failure ('test-vMF.R:102:3'): vMF matches for simulated weights, ignoring SE, which shouldn't match ── Expected `dir3_m` to equal `vMF_m(Y)`. Differences: `actual`: -0.579 0.722 -0.380 `expected`: -0.384 0.824 -0.417 Backtrace: ▆ 1. ├─testthat::expect_error(expect_equal(dir3_m, vMF_m(Y)), "not equal to") at test-vMF.R:102:3 2. │ └─testthat:::expect_condition_matching_(...) 3. │ └─testthat:::quasi_capture(...) 4. │ ├─testthat (local) .capture(...) 5. │ │ └─base::withCallingHandlers(...) 6. │ └─rlang::eval_bare(quo_get_expr(.quo), quo_get_env(.quo)) 7. └─testthat::expect_equal(dir3_m, vMF_m(Y)) ── Failure ('test-vMF.R:102:3'): vMF matches for simulated weights, ignoring SE, which shouldn't match ── Expected `expect_equal(dir3_m, vMF_m(Y))` to throw a error. [ FAIL 2 | WARN 0 | SKIP 18 | PASS 427 ] Error: ! Test failures. Execution halted Package: scrutiny Check: tests New result: ERROR Running ‘testthat.R’ [63s/64s] Running the tests in ‘tests/testthat.R’ failed. Complete output: > > > library(testthat) > library(scrutiny) > > test_check("scrutiny") Saving _problems/test-decimal-places-130.R Saving _problems/test-seq-predicates-141.R [ FAIL 2 | WARN 0 | SKIP 0 | PASS 525 ] ══ Failed tests ════════════════════════════════════════════════════════════════ ── Error ('test-decimal-places.R:130:3'): `decimal_places_scalar()` conditions work as expected ── Error in `testthat::expect(act$is_na, msg_error)`: `failure_message` must be a character vector, not `NULL`. Backtrace: ▆ 1. ├─NA %>% decimal_places_scalar() %>% expect_na() at test-decimal-places.R:130:3 2. └─scrutiny:::expect_na(.) 3. └─testthat::expect(failure_message = msg_error) at ./helper-expectations.R:18:3 4. └─testthat:::check_character(failure_message) 5. └─testthat:::stop_input_type(...) 6. └─rlang::abort(message, ..., call = call, arg = arg) ── Error ('test-seq-predicates.R:141:3'): `is_seq_dispersed()` passes its special tests, returning `NA` ── Error in `testthat::expect(act$is_na, msg_error)`: `failure_message` must be a character vector, not `NULL`. Backtrace: ▆ 1. ├─... %>% expect_na() at test-seq-predicates.R:141:3 2. └─scrutiny:::expect_na(.) 3. └─testthat::expect(failure_message = msg_error) at ./helper-expectations.R:18:3 4. └─testthat:::check_character(failure_message) 5. └─testthat:::stop_input_type(...) 6. └─rlang::abort(message, ..., call = call, arg = arg) [ FAIL 2 | WARN 0 | SKIP 0 | PASS 525 ] Error: ! Test failures. Execution halted Package: seqminer Check: tests New result: ERROR Running ‘testthat.R’ [2s/3s] Running the tests in ‘tests/testthat.R’ failed. Complete output: > ## follow http://adv-r.had.co.nz/Testing.html > print(sprintf("Test seqminer [version %s]", packageVersion("seqminer"))) [1] "Test seqminer [version 9.7]" > print("Platform") [1] "Platform" > print(str(.Platform)) List of 8 $ OS.type : chr "unix" $ file.sep : chr "/" $ dynlib.ext: chr ".so" $ GUI : chr "X11" $ endian : chr "little" $ pkgType : chr "source" $ path.sep : chr ":" $ r_arch : chr "" NULL > > print("Sys.info()") [1] "Sys.info()" > print(Sys.info()) sysname "Linux" release "6.16.7+deb14-amd64" version "#1 SMP PREEMPT_DYNAMIC Debian 6.16.7-1 (2025-09-11)" nodename "anduin2" machine "x86_64" login "hornik" user "hornik" effective_user "hornik" > > print(citation("seqminer")) To cite package 'seqminer' in publications use: Zhan X, Liu D (2015). "SEQMINER: An R-Package to Facilitate the Functional Interpretation of Sequence-Based Associations." _Genetic Epidemiology_, *39*(8), 1242. doi:10.1002/gepi.21918 . seqminer version 9.7, . A BibTeX entry for LaTeX users is @Article{, title = {SEQMINER: An R-Package to Facilitate the Functional Interpretation of Sequence-Based Associations}, author = {Xiaowei Zhan and Dajiang J Liu}, journal = {Genetic Epidemiology}, volume = {39}, number = {8}, pages = {1242}, year = {2015}, note = {seqminer version 9.7}, web = {http:/zhanxw.github.io/seqminer}, publisher = {Wiley Online Library}, url = {http://dx.doi.org/10.1002/gepi.21918}, doi = {10.1002/gepi.21918}, } > > library(testthat) > library(seqminer) > ## ## test code are under inst/tests > ## ## test_package("seqminer", reporter="tap") > ## test_package("seqminer") > test_check("seqminer") 1 gene/region to be extracted. Read score tests... In study 0 Done read score file: /home/hornik/tmp/CRAN_recheck/seqminer.Rcheck/seqminer/test-triallelic/bi.MetaScore.assoc.gz Read cov files ... In study 0 Done read cov file: /home/hornik/tmp/CRAN_recheck/seqminer.Rcheck/seqminer/test-triallelic/bi.MetaCov.assoc.gz Finished calculation. [1] "1:1" "1:2" "1:3" [[1]] [1] "G" "G" "G" [[1]] [,1] [,2] [,3] [1,] 0.5126420 -0.0427202 0.0427202 [2,] -0.0427202 0.1993610 -0.0284801 [3,] 0.0427202 -0.0284801 0.1139210 Skipped: encounter a duplicated site: [ 1:1 ] in file [ /home/hornik/tmp/CRAN_recheck/seqminer.Rcheck/seqminer/test-triallelic/tri.MetaScore.assoc.gz ] 1 gene/region to be extracted. Read score tests... In study 0 Error: encounter a duplicated location: [ 1:1 ] in file [ /home/hornik/tmp/CRAN_recheck/seqminer.Rcheck/seqminer/test-triallelic/tri.MetaScore.assoc.gz ], will overwrite previous results! Done read score file: /home/hornik/tmp/CRAN_recheck/seqminer.Rcheck/seqminer/test-triallelic/tri.MetaScore.assoc.gz Read cov files ... In study 0 Done read cov file: /home/hornik/tmp/CRAN_recheck/seqminer.Rcheck/seqminer/test-triallelic/tri.MetaCov.assoc.gz Finished calculation. [1] "1:1" "1:3" [[1]] [1] "T" "G" [[1]] [,1] [,2] [1,] 0.1993610 -0.0284801 [2,] -0.0284801 0.1139210 1 gene/region to be extracted. Read score tests... In study 0 Done read score file: /home/hornik/tmp/CRAN_recheck/seqminer.Rcheck/seqminer/test-triallelic/tri.MetaScore.assoc.gz Read cov files ... In study 0 Done read cov file: /home/hornik/tmp/CRAN_recheck/seqminer.Rcheck/seqminer/test-triallelic/tri.MetaCov.assoc.gz Finished calculation. [1] "1:1_A/G" "1:1_A/T" "1:3_A/G" [[1]] [1] "G" "T" "G" [[1]] [,1] [,2] [,3] [1,] 0.5126420 -0.0427202 0.0427202 [2,] -0.0427202 0.1993610 -0.0284801 [3,] 0.0427202 -0.0284801 0.1139210 [1] "=============" Total 3 line loaded, now put them to matrix [ 3 x 3 ] in R ... 1:1 1:2 1:3 1:1 0.5126420 -0.0427202 0.0427202 1:2 -0.0427202 0.1993610 -0.0284801 1:3 0.0427202 -0.0284801 0.1139210 1:1 1:2 1:3 1:1 0.5126420 -0.0427202 0.0427202 1:2 -0.0427202 0.1993610 -0.0284801 1:3 0.0427202 -0.0284801 0.1139210 Total 3 line loaded, now put them to matrix [ 3 x 3 ] in R ... 1:1 1:1/1 1:3 1:1 0.5126420 -0.0427202 0.0427202 1:1/1 -0.0427202 0.1993610 -0.0284801 1:3 0.0427202 -0.0284801 0.1139210 [1] "=============" $pos [1] 1 2 3 $ref [1] "A" "A" "A" $alt [1] "G" "G" "G" $nSample [1] 9 9 9 $af [1] 0.3333330 0.1111110 0.0555556 $ac [1] 6 2 1 $callRate [1] 1 1 1 $hwe [1] "1" "1" "1" $nref [1] 4 7 8 $nhet [1] 4 2 1 $nalt [1] 1 0 0 $ustat [1] 0.283603 0.000000 0.325521 $vstat [1] 2.14797 1.33950 1.01256 $effect [1] 0.0614687 0.0000000 0.3174930 $pVal [1] 0.894958 1.000000 0.747845 $anno [1] "" "" "" $annoFull [1] "" "" "" $pos [1] 1 1 3 $ref [1] "A" "A" "A" $alt [1] "G" "T" "G" $nSample [1] 9 9 9 $af [1] 0.3333330 0.1111110 0.0555556 $ac [1] 6 2 1 $callRate [1] 1 1 1 $hwe [1] "1" "1" "1" $nref [1] 4 7 8 $nhet [1] 4 2 1 $nalt [1] 1 0 0 $ustat [1] 0.283603 0.000000 0.325521 $vstat [1] 2.14797 1.33950 1.01256 $effect [1] 0.0614687 0.0000000 0.3174930 $pVal [1] 0.894958 1.000000 0.747845 $anno [1] "" "" "" $annoFull [1] "" "" "" Skipped: encounter a duplicated site: [ 1:1 ] in file [ /home/hornik/tmp/CRAN_recheck/seqminer.Rcheck/seqminer/test-triallelic/test2.study1.MetaScore.assoc.gz ] 1 gene/region to be extracted. Read score tests... In study 0 Error: encounter a duplicated location: [ 1:1 ] in file [ /home/hornik/tmp/CRAN_recheck/seqminer.Rcheck/seqminer/test-triallelic/test2.study1.MetaScore.assoc.gz ], will overwrite previous results! Done read score file: /home/hornik/tmp/CRAN_recheck/seqminer.Rcheck/seqminer/test-triallelic/test2.study1.MetaScore.assoc.gz In study 1 Done read score file: /home/hornik/tmp/CRAN_recheck/seqminer.Rcheck/seqminer/test-triallelic/test2.study2.MetaScore.assoc.gz Read cov files ... In study 0 Done read cov file: /home/hornik/tmp/CRAN_recheck/seqminer.Rcheck/seqminer/test-triallelic/test2.study1.MetaCov.assoc.gz In study 1 Done read cov file: /home/hornik/tmp/CRAN_recheck/seqminer.Rcheck/seqminer/test-triallelic/test2.study2.MetaCov.assoc.gz Finished calculation. 1 gene/region to be extracted. Read score tests... In study 0 Done read score file: /home/hornik/tmp/CRAN_recheck/seqminer.Rcheck/seqminer/test-triallelic/test2.study1.MetaScore.assoc.gz In study 1 Done read score file: /home/hornik/tmp/CRAN_recheck/seqminer.Rcheck/seqminer/test-triallelic/test2.study2.MetaScore.assoc.gz Read cov files ... In study 0 Done read cov file: /home/hornik/tmp/CRAN_recheck/seqminer.Rcheck/seqminer/test-triallelic/test2.study1.MetaCov.assoc.gz In study 1 Done read cov file: /home/hornik/tmp/CRAN_recheck/seqminer.Rcheck/seqminer/test-triallelic/test2.study2.MetaCov.assoc.gz Finished calculation. Loading objects: apoe cfh cfh.all cfh.nonsyn cfh.nonsyn.2 cfh.range cfh.syn cfh.syn.2 out.gene.1 out.gene.2 out.gene.3 out.range.1 ssss --------------- test readVCFToMatrixByRange --------------- 1 region to be extracted. 1 region to be extracted. 2 region to be extracted. 1 region to be extracted. 1 region to be extracted. range of [ CFH ] is [ 1:196621007-196716634 ] 1 region to be extracted. range of [ APOE ] is [ 19:45409038-45412650 ] 1 region to be extracted. range of [ ssss ] is [ ] range of [ CFH ] is [ 1:196621007-196716634 ] range = 1:196621007-196716634 range of [ CFH ] is [ 1:196621007-196716634 ] range = 1:196621007-196716634 range of [ CFH ] is [ 1:196621007-196716634 ] range = 1:196621007-196716634 1 region to be extracted. 1 region to be extracted. 1 region to be extracted. range of [ CFH ] is [ 1:196621007-196716634 ] range of [ CFH ] is [ 1:196621007-196716634 ] range = 1:196621007-196716634 start file = /home/hornik/tmp/CRAN_recheck/seqminer.Rcheck/seqminer/plink/all.anno.filtered.extract Wed Nov 12 11:53:27 2025 - read bim Wed Nov 12 11:53:27 2025 - read fam extract 2 marker and 3 sample out of 166 marker and 3 sample Wed Nov 12 11:53:27 2025 - read bed Binary PLINK BED file is ready to read build a look-up table assigned 6 values allocate dim and dimnames Wed Nov 12 11:53:27 2025 - end --------------- test openPlink --------------- --------------- test readPlinkBySubscript --------------- start file = /home/hornik/tmp/CRAN_recheck/seqminer.Rcheck/seqminer/plink/all.anno.filtered.extract.bed extract 2 marker and 3 sample out of 166 marker and 3 sample Binary PLINK BED file is ready to read build a look-up table assigned 6 values allocate dim Wed Nov 12 11:53:27 2025 - end --------------- test readSingleChromosomeVCFToMatrixByRange --------------- fsize = 2688 query [0, 0] Found 1 results found 1 position, e.g. 0 2384 Inferred 3 samples from header 1 region to be extracted. query [196621007, 196716634] Found 166 results found 166 position, e.g. 196623337 2454 --------------- test createSingleChromosomeVCFIndex --------------- header line has 3 samples offset = 2384 Indexing finished with 3 samples and 166 markers created index file [ /home/hornik/tmp/scratch/RtmpUdWBCd/file144bd94c288d08 ] Saving _problems/test-vcf-167.R --------------- test readSingleChromosomeBCFToMatrixByRange --------------- fsize = 2688 l_text = 4206 Total contig parse = 1, total header index used = 47 Inferred 3 samples from header 1 region to be extracted. query [196621006, 196716633] Found 166 results 196623336 4215 found 166 position, e.g. 196623336 4215 sampleNames.size() = 3, markerNames.size() = 166 --------------- test createSingleChromosomeBCFIndex --------------- l_text = 4206 Total contig parse = 1, total header index used = 47 offset_header = 4135 sample size = 3 last character is s[after_chrom_size-1] = 0 Indexing finished with 3 samples and 166 markers created index file [ /home/hornik/tmp/scratch/RtmpUdWBCd/file144bd964428d86 ] Saving _problems/test-vcf-181.R [ FAIL 2 | WARN 0 | SKIP 0 | PASS 72 ] ══ Failed tests ════════════════════════════════════════════════════════════════ ── Error ('test-vcf.R:167:5'): createSingleChromosomeVCFIndex ────────────────── Error in `expect(nchar(cfh) > 0)`: `failure_message` must be a character vector, not absent. Backtrace: ▆ 1. └─testthat::expect(failure_message = ) at test-vcf.R:167:5 2. └─testthat:::check_character(failure_message) 3. └─testthat:::stop_input_type(...) 4. └─rlang::abort(message, ..., call = call, arg = arg) ── Error ('test-vcf.R:181:5'): createSingleChromosomeBCFIndex ────────────────── Error in `expect(nchar(cfh) > 0)`: `failure_message` must be a character vector, not absent. Backtrace: ▆ 1. └─testthat::expect(failure_message = ) at test-vcf.R:181:5 2. └─testthat:::check_character(failure_message) 3. └─testthat:::stop_input_type(...) 4. └─rlang::abort(message, ..., call = call, arg = arg) [ FAIL 2 | WARN 0 | SKIP 0 | PASS 72 ] Error: ! Test failures. Execution halted Package: serocalculator Check: tests New result: ERROR Running ‘spelling.R’ [0s/0s] Running ‘testthat.R’ [23s/37s] Running the tests in ‘tests/testthat.R’ failed. Complete output: > # This file is part of the standard setup for testthat. > # It is recommended that you do not modify it. > # > # Where should you do additional test configuration? > # Learn more about the roles of various files in: > # * https://r-pkgs.org/testing-design.html#sec-tests-files-overview > # * https://testthat.r-lib.org/articles/special-files.html > > library(testthat) > library(serocalculator) > > test_check("serocalculator", stop_on_warning = TRUE) Attaching package: 'magrittr' The following objects are masked from 'package:testthat': equals, is_less_than, not Attaching package: 'dplyr' The following objects are masked from 'package:stats': filter, lag The following objects are masked from 'package:base': intersect, setdiff, setequal, union Saving _problems/test-as_curve_params-33.R Proceeding to use "`Age`" Attaching package: 'tidyr' The following object is masked from 'package:magrittr': extract i Data has been stratified. i Here are the strata that will be analyzed: # A tibble: 2 x 3 Stratum catchment n 1 Stratum 1 aku 53 2 Stratum 2 kgh 47 starting new stratum: Stratum 1 # A tibble: 1 x 3 Stratum catchment n 1 Stratum 1 aku 53 nrow(curve_params) = 200 Initial negative log-likelihood: 271.620368416703 about to call `nlm()` iteration = 0 Step: [1] 0 Parameter: [1] -2.302585 Function Value [1] 271.6204 Gradient: [1] -12.19684 iteration = 1 Step: [1] 0.3 Parameter: [1] -2.002585 Function Value [1] 269.4844 Gradient: [1] -1.521341 iteration = 2 Step: [1] 0.0427523 Parameter: [1] -1.959833 Function Value [1] 269.4571 Gradient: [1] 0.2566987 iteration = 3 Step: [1] -0.006172225 Parameter: [1] -1.966005 Function Value [1] 269.4563 Gradient: [1] -0.003952892 iteration = 4 Parameter: [1] -1.965911 Function Value [1] 269.4563 Gradient: [1] -9.975515e-06 Relative gradient close to zero. Current iterate is probably solution. Elapsed time: user system elapsed 0.113 0.000 0.113 starting new stratum: Stratum 2 # A tibble: 1 x 3 Stratum catchment n 1 Stratum 2 kgh 47 nrow(curve_params) = 200 Initial negative log-likelihood: 261.759517614626 about to call `nlm()` iteration = 0 Step: [1] 0 Parameter: [1] -2.302585 Function Value [1] 261.7595 Gradient: [1] -23.7431 iteration = 1 Step: [1] 0.498822 Parameter: [1] -1.803763 Function Value [1] 253.5433 Gradient: [1] -7.990928 iteration = 2 Step: [1] 0.2530477 Parameter: [1] -1.550715 Function Value [1] 252.8398 Gradient: [1] 2.718074 iteration = 3 Step: [1] -0.06422655 Parameter: [1] -1.614942 Function Value [1] 252.7582 Gradient: [1] -0.161689 iteration = 4 Step: [1] 0.003606105 Parameter: [1] -1.611336 Function Value [1] 252.7579 Gradient: [1] -0.002816973 iteration = 5 Parameter: [1] -1.611272 Function Value [1] 252.7579 Gradient: [1] 3.069238e-06 Relative gradient close to zero. Current iterate is probably solution. Elapsed time: user system elapsed 0.12 0.00 0.12 i Elapsed time for loop over strata: user system elapsed 0.936 0.020 0.957 i Data has been stratified. i Here are the strata that will be analyzed: # A tibble: 2 x 3 Stratum catchment n 1 Stratum 1 aku 53 2 Stratum 2 kgh 47 Setting up parallel processing with `num_cores` = 2. i Elapsed time for parallelized code: user system elapsed 0.000 0.004 0.615 Attaching package: 'readr' The following objects are masked from 'package:testthat': edition_get, local_edition [ FAIL 1 | WARN 0 | SKIP 15 | PASS 33 ] ══ Skipped tests (15) ══════════════════════════════════════════════════════════ • On CRAN (15): 'test-ab0.R:1:1', 'test-as_noise_params.R:13:1', 'test-autoplot.pop_data.R:19:1', 'test-autoplot.pop_data.R:28:1', 'test-autoplot.pop_data.R:37:1', 'test-class_attributes.R:9:1', 'test-df_to_array.R:1:1', 'test-est.incidence.R:1:1', 'test-est.incidence.by.R:35:1', 'test-f_dev.R:1:1', 'test-log_likelihood.R:1:1', 'test-plot_curve_params_one_ab.R:1:1', 'test-stratify_data.R:1:1', 'test-summary.pop_data.R:12:1', 'test-summary.pop_data.R:16:1' ══ Failed tests ════════════════════════════════════════════════════════════════ ── Error ('test-as_curve_params.R:33:3'): `as_curve_params()` produces expected results ── Error in `test_data %>% ssdtools:::expect_snapshot_data(name = "curve-data")`: object 'expect_snapshot_data' not found Backtrace: ▆ 1. └─test_data %>% ssdtools:::expect_snapshot_data(name = "curve-data") at test-as_curve_params.R:33:3 [ FAIL 1 | WARN 0 | SKIP 15 | PASS 33 ] Deleting unused snapshots: 'as_curve_params/curve-data.csv' Error: ! Test failures. Execution halted Package: sf Check: tests New result: NOTE Running ‘aggregate.R’ [1s/1s] Comparing ‘aggregate.Rout’ to ‘aggregate.Rout.save’ ... OK Running ‘cast.R’ [2s/2s] Comparing ‘cast.Rout’ to ‘cast.Rout.save’ ... OK Running ‘crs.R’ [1s/2s] Comparing ‘crs.Rout’ to ‘crs.Rout.save’ ... OK Running ‘dist.R’ [1s/1s] Comparing ‘dist.Rout’ to ‘dist.Rout.save’ ... OK Running ‘dplyr.R’ [4s/4s] Comparing ‘dplyr.Rout’ to ‘dplyr.Rout.save’ ... OK Running ‘empty.R’ [1s/1s] Comparing ‘empty.Rout’ to ‘empty.Rout.save’ ... OK Running ‘full.R’ [2s/2s] Comparing ‘full.Rout’ to ‘full.Rout.save’ ... OK Running ‘gdal_geom.R’ [2s/2s] Comparing ‘gdal_geom.Rout’ to ‘gdal_geom.Rout.save’ ... OK Running ‘geos.R’ [29s/29s] Comparing ‘geos.Rout’ to ‘geos.Rout.save’ ... OK Running ‘graticule.R’ [4s/4s] Comparing ‘graticule.Rout’ to ‘graticule.Rout.save’ ... OK Running ‘grid.R’ [1s/1s] Comparing ‘grid.Rout’ to ‘grid.Rout.save’ ... OK Running ‘maps.R’ [3s/3s] Comparing ‘maps.Rout’ to ‘maps.Rout.save’ ... OK Running ‘plot.R’ [8s/8s] Comparing ‘plot.Rout’ to ‘plot.Rout.save’ ... OK Running ‘read.R’ [3s/3s] Comparing ‘read.Rout’ to ‘read.Rout.save’ ... OK Running ‘roundtrip.R’ [2s/3s] Comparing ‘roundtrip.Rout’ to ‘roundtrip.Rout.save’ ... OK Running ‘s2.R’ [2s/2s] Comparing ‘s2.Rout’ to ‘s2.Rout.save’ ... OK Running ‘sample.R’ [1s/1s] Comparing ‘sample.Rout’ to ‘sample.Rout.save’ ... OK Running ‘sfc.R’ [14s/14s] Comparing ‘sfc.Rout’ to ‘sfc.Rout.save’ ...547a548,551 > The following object is masked from 'package:testthat': > > matches > Running ‘sfg.R’ [1s/1s] Comparing ‘sfg.Rout’ to ‘sfg.Rout.save’ ... OK Running ‘spatstat.R’ [4s/5s] Comparing ‘spatstat.Rout’ to ‘spatstat.Rout.save’ ... OK Running ‘stars.R’ [4s/4s] Comparing ‘stars.Rout’ to ‘stars.Rout.save’ ... OK Running ‘testthat.R’ [19s/20s] Running ‘units.R’ [1s/1s] Comparing ‘units.Rout’ to ‘units.Rout.save’ ... OK Running ‘wkb.R’ [1s/1s] Comparing ‘wkb.Rout’ to ‘wkb.Rout.save’ ... OK Package: shiny.benchmark Check: tests New result: ERROR Running ‘testthat.R’ [4s/4s] Running the tests in ‘tests/testthat.R’ failed. Complete output: > library(testthat) > library(shiny.benchmark) > > test_check("shiny.benchmark") Saving _problems/test-load_example-9.R Saving _problems/test-load_example-52.R Saving _problems/test-load_example-74.R [ FAIL 3 | WARN 0 | SKIP 0 | PASS 7 ] ══ Failed tests ════════════════════════════════════════════════════════════════ ── Error ('test-load_example.R:9:5'): Load example creates correct structure ─── Error: `local_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `local_mocked_bindings()` instead. Backtrace: ▆ 1. └─base::eval(...) 2. └─base::eval(...) 3. └─testthat::local_mock(menu = function(...) stop("Opps, shouldn't reach this")) at test-load_example.R:9:5 4. └─lifecycle::deprecate_stop("3.2.0", "local_mock()", "local_mocked_bindings()") 5. └─lifecycle:::deprecate_stop0(msg) 6. └─rlang::cnd_signal(...) ── Error ('test-load_example.R:52:5'): Does not create load_examples on non-existing directory ── Error: `local_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `local_mocked_bindings()` instead. Backtrace: ▆ 1. └─base::eval(...) 2. └─base::eval(...) 3. └─testthat::local_mock(menu = function(...) stop("Opps, shouldn't reach this")) at test-load_example.R:52:5 4. └─lifecycle::deprecate_stop("3.2.0", "local_mock()", "local_mocked_bindings()") 5. └─lifecycle:::deprecate_stop0(msg) 6. └─rlang::cnd_signal(...) ── Error ('test-load_example.R:74:5'): Does not create load_examples if there is a file in directory ── Error: `local_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `local_mocked_bindings()` instead. Backtrace: ▆ 1. └─base::eval(...) 2. └─base::eval(...) 3. └─testthat::local_mock(menu = function(...) 2) at test-load_example.R:74:5 4. └─lifecycle::deprecate_stop("3.2.0", "local_mock()", "local_mocked_bindings()") 5. └─lifecycle:::deprecate_stop0(msg) 6. └─rlang::cnd_signal(...) [ FAIL 3 | WARN 0 | SKIP 0 | PASS 7 ] Error: ! Test failures. Execution halted Package: shinyShortcut Check: tests New result: ERROR Running ‘testthat.R’ [1s/1s] Running the tests in ‘tests/testthat.R’ failed. Complete output: > library(testthat) > library(shinyShortcut) > > test_check("shinyShortcut") Saving _problems/test-shinyShortcut-31.R [ FAIL 1 | WARN 0 | SKIP 0 | PASS 0 ] ══ Failed tests ════════════════════════════════════════════════════════════════ ── Error ('test-shinyShortcut.R:5:3'): ShinyShorcut returns the correct files ── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test-shinyShortcut.R:5:3 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) [ FAIL 1 | WARN 0 | SKIP 0 | PASS 0 ] Error: ! Test failures. Execution halted Package: SIAtools Check: tests New result: ERROR Running ‘spelling.R’ [0s/0s] Running ‘testthat.R’ [14s/16s] Running the tests in ‘tests/testthat.R’ failed. Complete output: > # This file is part of the standard setup for testthat. > # It is recommended that you do not modify it. > # > # Where should you do additional test configuration? > # Learn more about the roles of various files in: > # * https://r-pkgs.org/testing-design.html#sec-tests-files-overview > # * https://testthat.r-lib.org/articles/special-files.html > > library(testthat) > library(SIAtools) > > test_check("SIAtools") v Restoring original working directory to '/home/hornik/tmp/CRAN_recheck/SIAtools.Rcheck/tests/testthat'... v Setting active project to "". v Deleting temporary project at '/home/hornik/tmp/scratch/RtmphTvIPH/file1389f96438fbab'... v Restoring original working directory to '/home/hornik/tmp/CRAN_recheck/SIAtools.Rcheck/tests/testthat'... v Setting active project to "". v Deleting temporary project at '/home/hornik/tmp/scratch/RtmphTvIPH/file1389f91503a732'... x There is no SIA Modules Manifest file at 'inst/sia/modules.yml' i Creating a new one. > 'inst/sia/modules.yml' YAML file with the SIA Modules Manifest has been created. Please inspect and edit. > 'R/sm_test.R' file with your new module has been created. You can write your code here. > Edit the title of your module in the YAML. > Chose a category from the options listed in the YAML. v Restoring original working directory to '/home/hornik/tmp/CRAN_recheck/SIAtools.Rcheck/tests/testthat'... v Setting active project to "". v Deleting temporary project at '/home/hornik/tmp/scratch/RtmphTvIPH/file1389f91aeeb43c'... i Setting working directory to '/home/hornik/tmp/scratch/RtmphTvIPH/file1389f9540cdae8'... v Restoring original working directory to '/home/hornik/tmp/CRAN_recheck/SIAtools.Rcheck/tests/testthat'... v Setting active project to "". v Deleting temporary project at '/home/hornik/tmp/scratch/RtmphTvIPH/file1389f9540cdae8'... i Adding Config/ShinyItemAnalysis/module: true to 'DESCRIPTION'. x There is no SIA Modules Manifest file at 'inst/sia/modules.yml' i Creating a new one. > 'inst/sia/modules.yml' YAML file with the SIA Modules Manifest has been created. Please inspect and edit. > 'R/sm_test.R' file with your new module has been created. You can write your code here. > Edit the title of your module in the YAML. > Chose a category from the options listed in the YAML. v Restoring original working directory to '/home/hornik/tmp/CRAN_recheck/SIAtools.Rcheck/tests/testthat'... v Setting active project to "". v Deleting temporary project at '/home/hornik/tmp/scratch/RtmphTvIPH/file1389f97e33024a'... i Adding Config/ShinyItemAnalysis/module: true to 'DESCRIPTION'. x There is no SIA Modules Manifest file at 'inst/sia/modules.yml' i Creating a new one. > 'inst/sia/modules.yml' YAML file with the SIA Modules Manifest has been created. Please inspect and edit. > 'R/sm_test.R' file with your new module has been created. You can write your code here. > Edit the title of your module in the YAML. > Chose a category from the options listed in the YAML. v Restoring original working directory to '/home/hornik/tmp/CRAN_recheck/SIAtools.Rcheck/tests/testthat'... v Setting active project to "". v Deleting temporary project at '/home/hornik/tmp/scratch/RtmphTvIPH/file1389f96125768a'... i Adding Config/ShinyItemAnalysis/module: true to 'DESCRIPTION'. x There is no SIA Modules Manifest file at 'inst/sia/modules.yml' i Creating a new one. > 'inst/sia/modules.yml' YAML file with the SIA Modules Manifest has been created. Please inspect and edit. > 'R/sm_test.R' file with your new module has been created. You can write your code here. > Edit the title of your module in the YAML. > Chose a category from the options listed in the YAML. v Restoring original working directory to '/home/hornik/tmp/CRAN_recheck/SIAtools.Rcheck/tests/testthat'... v Setting active project to "". v Deleting temporary project at '/home/hornik/tmp/scratch/RtmphTvIPH/file1389f94be81344'... v Restoring original working directory to '/home/hornik/tmp/CRAN_recheck/SIAtools.Rcheck/tests/testthat'... v Setting active project to "". v Deleting temporary project at '/home/hornik/tmp/scratch/RtmphTvIPH/file1389f934f1750b'... i Setting working directory to '/home/hornik/tmp/scratch/RtmphTvIPH/file1389f95fb3f8b4'... v Restoring original working directory to '/home/hornik/tmp/CRAN_recheck/SIAtools.Rcheck/tests/testthat'... v Setting active project to "". v Deleting temporary project at '/home/hornik/tmp/scratch/RtmphTvIPH/file1389f95fb3f8b4'... i Setting working directory to '/home/hornik/tmp/scratch/RtmphTvIPH/file1389f926fbbd0b'... x There is no SIA Modules Manifest file at 'inst/sia/modules.yml' i Creating a new one. > 'inst/sia/modules.yml' YAML file with the SIA Modules Manifest has been created. Please inspect and edit. > 'R/sm_test.R' file with your new module has been created. You can write your code here. > Edit the title of your module in the YAML. > Chose a category from the options listed in the YAML. v Restoring original working directory to '/home/hornik/tmp/CRAN_recheck/SIAtools.Rcheck/tests/testthat'... v Setting active project to "". v Deleting temporary project at '/home/hornik/tmp/scratch/RtmphTvIPH/file1389f926fbbd0b'... i Setting working directory to '/home/hornik/tmp/scratch/RtmphTvIPH/file1389f9214380b8'... v Restoring original working directory to '/home/hornik/tmp/CRAN_recheck/SIAtools.Rcheck/tests/testthat'... v Setting active project to "". v Deleting temporary project at '/home/hornik/tmp/scratch/RtmphTvIPH/file1389f9214380b8'... i Setting working directory to '/home/hornik/tmp/scratch/RtmphTvIPH/file1389f916da31bd'... x There is no SIA Modules Manifest file at 'inst/sia/modules.yml' i Creating a new one. > Edit the title of your module in the YAML. v Restoring original working directory to '/home/hornik/tmp/CRAN_recheck/SIAtools.Rcheck/tests/testthat'... v Setting active project to "". v Deleting temporary project at '/home/hornik/tmp/scratch/RtmphTvIPH/file1389f916da31bd'... ! This package doesn't declare that it contains any SIA modules (there is no line reading Config/ShinyItemAnalysis/module: true in the 'DESCRIPTION'). i Trying to obtain and read SIA Modules Manifest nevertheless... x There is no SIA Modules Manifest file at 'inst/sia/modules.yml' v Restoring original working directory to '/home/hornik/tmp/CRAN_recheck/SIAtools.Rcheck/tests/testthat'... v Setting active project to "". v Deleting temporary project at '/home/hornik/tmp/scratch/RtmphTvIPH/file1389f94a029166'... i Adding Config/ShinyItemAnalysis/module: true to 'DESCRIPTION'. x There is no SIA Modules Manifest file at 'inst/sia/modules.yml' i Creating a new one. > 'inst/sia/modules.yml' YAML file with the SIA Modules Manifest has been created. Please inspect and edit. > 'R/sm_test.R' file with your new module has been created. You can write your code here. > Edit the title of your module in the YAML. > Chose a category from the options listed in the YAML. v Restoring original working directory to '/home/hornik/tmp/CRAN_recheck/SIAtools.Rcheck/tests/testthat'... v Setting active project to "". v Deleting temporary project at '/home/hornik/tmp/scratch/RtmphTvIPH/file1389f966cafbd2'... i Adding Config/ShinyItemAnalysis/module: true to 'DESCRIPTION'. x There is no SIA Modules Manifest file at 'inst/sia/modules.yml' i Creating a new one. > 'inst/sia/modules.yml' YAML file with the SIA Modules Manifest has been created. Please inspect and edit. > 'R/sm_test1.R' file with your new module has been created. You can write your code here. > 'inst/sia/modules.yml' YAML file with the SIA Modules Manifest has been created. Please inspect and edit. > 'R/sm_test2.R' file with your new module has been created. You can write your code here. v Restoring original working directory to '/home/hornik/tmp/CRAN_recheck/SIAtools.Rcheck/tests/testthat'... v Setting active project to "". v Deleting temporary project at '/home/hornik/tmp/scratch/RtmphTvIPH/file1389f95d7fe9b3'... ! This package doesn't declare that it contains any SIA modules (there is no line reading Config/ShinyItemAnalysis/module: true in the 'DESCRIPTION'). i Trying to obtain and read SIA Modules Manifest nevertheless... ! This package doesn't declare that it contains any SIA modules (there is no line reading Config/ShinyItemAnalysis/module: true in the 'DESCRIPTION'). i Trying to obtain and read SIA Modules Manifest nevertheless... ! This package doesn't declare that it contains any SIA modules (there is no line reading Config/ShinyItemAnalysis/module: true in the 'DESCRIPTION'). i Trying to obtain and read SIA Modules Manifest nevertheless... x The SIA Modules Manifest file at 'inst/sia/modules.yml' is corrupt (not a list). v Restoring original working directory to '/home/hornik/tmp/CRAN_recheck/SIAtools.Rcheck/tests/testthat'... v Setting active project to "". v Deleting temporary project at '/home/hornik/tmp/scratch/RtmphTvIPH/file1389f91f2076d2'... i Adding Config/ShinyItemAnalysis/module: true to 'DESCRIPTION'. x There is no SIA Modules Manifest file at 'inst/sia/modules.yml' i Creating a new one. > 'inst/sia/modules.yml' YAML file with the SIA Modules Manifest has been created. Please inspect and edit. > 'R/sm_test.R' file with your new module has been created. You can write your code here. > Edit the title of your module in the YAML. > Chose a category from the options listed in the YAML. # A tibble: 1 x 5 module_id title category binding_ui binding_server 1 sm_test CHANGE THIS TO YOUR MODULE'S TIT~ Scores ~ sm_test_ui sm_test_server v Restoring original working directory to '/home/hornik/tmp/CRAN_recheck/SIAtools.Rcheck/tests/testthat'... v Setting active project to "". v Deleting temporary project at '/home/hornik/tmp/scratch/RtmphTvIPH/file1389f975298d33'... i Setting working directory to '/home/hornik/tmp/scratch/RtmphTvIPH/file1389f96c672385'... v Restoring original working directory to '/home/hornik/tmp/CRAN_recheck/SIAtools.Rcheck/tests/testthat'... v Setting active project to "". v Deleting temporary project at '/home/hornik/tmp/scratch/RtmphTvIPH/file1389f96c672385'... i Setting working directory to '/home/hornik/tmp/scratch/RtmphTvIPH/file1389f9d0c1c8b'... v Restoring original working directory to '/home/hornik/tmp/CRAN_recheck/SIAtools.Rcheck/tests/testthat'... v Setting active project to "". v Deleting temporary project at '/home/hornik/tmp/scratch/RtmphTvIPH/file1389f9d0c1c8b'... i Setting working directory to '/home/hornik/tmp/scratch/RtmphTvIPH/file1389f95483c451'... v Restoring original working directory to '/home/hornik/tmp/CRAN_recheck/SIAtools.Rcheck/tests/testthat'... v Setting active project to "". v Deleting temporary project at '/home/hornik/tmp/scratch/RtmphTvIPH/file1389f95483c451'... i Setting working directory to '/home/hornik/tmp/scratch/RtmphTvIPH/file1389f95a36f552'... v Restoring original working directory to '/home/hornik/tmp/CRAN_recheck/SIAtools.Rcheck/tests/testthat'... v Setting active project to "". v Deleting temporary project at '/home/hornik/tmp/scratch/RtmphTvIPH/file1389f95a36f552'... i Setting working directory to '/home/hornik/tmp/scratch/RtmphTvIPH/file1389f93487ecee'... v Restoring original working directory to '/home/hornik/tmp/CRAN_recheck/SIAtools.Rcheck/tests/testthat'... v Setting active project to "". v Deleting temporary project at '/home/hornik/tmp/scratch/RtmphTvIPH/file1389f93487ecee'... i Setting working directory to '/home/hornik/tmp/scratch/RtmphTvIPH/file1389f95655dfad'... v Restoring original working directory to '/home/hornik/tmp/CRAN_recheck/SIAtools.Rcheck/tests/testthat'... v Setting active project to "". v Deleting temporary project at '/home/hornik/tmp/scratch/RtmphTvIPH/file1389f95655dfad'... i Adding Config/ShinyItemAnalysis/module: true to 'DESCRIPTION'. x There is no SIA Modules Manifest file at 'inst/sia/modules.yml' i Creating a new one. > 'inst/sia/modules.yml' YAML file with the SIA Modules Manifest has been created. Please inspect and edit. > 'R/sm_test.R' file with your new module has been created. You can write your code here. > Edit the title of your module in the YAML. > Chose a category from the options listed in the YAML. Writing 'NAMESPACE' i Loading file1389f96aff3ee0 x sm_test.R:25: @description Could not resolve link to topic "sm_test" in the dependencies or base packages. i If you haven't documented "sm_test" yet, or just changed its name, this is normal. Once "sm_test" is documented, this warning goes away. i Make sure that the name of the topic is spelled correctly. i Always list the linked package as a dependency. i Alternatively, you can fully qualify the link with a package name. Writing 'sm_test.Rd' Writing 'sm_test_internal.Rd' i Loading file1389f96aff3ee0 v Restoring original working directory to '/home/hornik/tmp/CRAN_recheck/SIAtools.Rcheck/tests/testthat'... v Setting active project to "". v Deleting temporary project at '/home/hornik/tmp/scratch/RtmphTvIPH/file1389f96aff3ee0'... i Adding Config/ShinyItemAnalysis/module: true to 'DESCRIPTION'. x There is no SIA Modules Manifest file at 'inst/sia/modules.yml' i Creating a new one. > 'inst/sia/modules.yml' YAML file with the SIA Modules Manifest has been created. Please inspect and edit. > 'R/sm_test.R' file with your new module has been created. You can write your code here. > Edit the title of your module in the YAML. > Chose a category from the options listed in the YAML. Writing 'NAMESPACE' i Loading file1389f95de6202b x sm_test.R:25: @description Could not resolve link to topic "sm_test" in the dependencies or base packages. i If you haven't documented "sm_test" yet, or just changed its name, this is normal. Once "sm_test" is documented, this warning goes away. i Make sure that the name of the topic is spelled correctly. i Always list the linked package as a dependency. i Alternatively, you can fully qualify the link with a package name. Writing 'sm_test.Rd' Writing 'sm_test_internal.Rd' i Loading file1389f95de6202b v Restoring original working directory to '/home/hornik/tmp/CRAN_recheck/SIAtools.Rcheck/tests/testthat'... v Setting active project to "". v Deleting temporary project at '/home/hornik/tmp/scratch/RtmphTvIPH/file1389f95de6202b'... i Adding Config/ShinyItemAnalysis/module: true to 'DESCRIPTION'. x There is no SIA Modules Manifest file at 'inst/sia/modules.yml' i Creating a new one. > 'inst/sia/modules.yml' YAML file with the SIA Modules Manifest has been created. Please inspect and edit. > 'R/sm_test.R' file with your new module has been created. You can write your code here. > Edit the title of your module in the YAML. > Chose a category from the options listed in the YAML. v Restoring original working directory to '/home/hornik/tmp/CRAN_recheck/SIAtools.Rcheck/tests/testthat'... v Setting active project to "". v Deleting temporary project at '/home/hornik/tmp/scratch/RtmphTvIPH/file1389f966f5e530'... [ FAIL 1 | WARN 0 | SKIP 4 | PASS 65 ] ══ Skipped tests (4) ═══════════════════════════════════════════════════════════ • On CRAN (4): 'test-add_module.R:33:1', 'test-add_module.R:66:1', 'test-add_module.R:73:1', 'test-add_module.R:92:1' ══ Failed tests ════════════════════════════════════════════════════════════════ ── Error ('test-get_modules.R:76:3'): manifest prints out correctly ──────────── Error in `parse(text = x)`: :5:19: unexpected symbol 4: sm_test: 5: title: CHANGE THIS ^ Backtrace: ▆ 1. ├─get_modules() %>% print(as_tibble = TRUE) %>% ... at test-get_modules.R:76:3 2. └─testthat::expect_snapshot_value(., style = "deparse", cran = TRUE) 3. └─testthat:::expect_snapshot_helper(...) 4. └─snapshotter$take_snapshot(...) 5. └─testthat (local) load(old_raw) 6. └─testthat:::reparse(x) 7. ├─base::eval(parse(text = x), env) 8. └─base::parse(text = x) [ FAIL 1 | WARN 0 | SKIP 4 | PASS 65 ] Error: ! Test failures. Execution halted Package: spaero Check: tests New result: ERROR Running ‘testthat.R’ [5s/5s] Running the tests in ‘tests/testthat.R’ failed. Complete output: > library(testthat) > library(spaero) > > test_check("spaero") Saving _problems/test-simulator-8.R [ FAIL 1 | WARN 4 | SKIP 7 | PASS 47 ] ══ Skipped tests (7) ═══════════════════════════════════════════════════════════ • On CRAN (6): 'test-simulator.R:60:3', 'test-simulator.R:82:3', 'test-simulator.R:157:3', 'test-simulator.R:190:3', 'test-stats.R:118:3', 'test-stats.R:237:3' • {earlywarnings} is not installed (1): 'test-stats.R:303:3' ══ Failed tests ════════════════════════════════════════════════════════════════ ── Error ('test-simulator.R:6:3'): Argument checking works ───────────────────── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test-simulator.R:6:3 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) [ FAIL 1 | WARN 4 | SKIP 7 | PASS 47 ] Error: ! Test failures. Execution halted Package: spatialsample Check: tests New result: ERROR Running ‘testthat.R’ [287s/145s] Running the tests in ‘tests/testthat.R’ failed. Complete output: > library(testthat) > library(spatialsample) > > sf::sf_extSoftVersion() GEOS GDAL proj.4 GDAL_with_GEOS USE_PROJ_H "3.14.1" "3.11.4" "9.7.0" "true" "true" PROJ "9.7.0" > > test_check("spatialsample") Starting 2 test processes. > test-buffer.R: Spherical geometry (s2) switched off > test-buffer.R: Spherical geometry (s2) switched on > test-autoplot.R: Only 4% of blocks contain any data > test-autoplot.R: i Check that your block sizes make sense for your data > test-compat-dplyr.R: > test-compat-dplyr.R: Attaching package: 'dplyr' > test-compat-dplyr.R: > test-compat-dplyr.R: The following objects are masked from 'package:stats': > test-compat-dplyr.R: > test-compat-dplyr.R: filter, lag > test-compat-dplyr.R: > test-compat-dplyr.R: The following objects are masked from 'package:base': > test-compat-dplyr.R: > test-compat-dplyr.R: intersect, setdiff, setequal, union > test-compat-dplyr.R: > test-spatial_block_cv.R: Spherical geometry (s2) switched off > test-spatial_block_cv.R: Spherical geometry (s2) switched on > test-spatial_nndm_cv.R: Linking to GEOS 3.14.1, GDAL 3.11.4, PROJ 9.7.0; sf_use_s2() is TRUE > test-spatial_vfold_cv.R: Spherical geometry (s2) switched off > test-spatial_vfold_cv.R: Spherical geometry (s2) switched on Saving _problems/test-spatial_vfold_cv-202.R [ FAIL 1 | WARN 0 | SKIP 26 | PASS 535 ] ══ Skipped tests (26) ══════════════════════════════════════════════════════════ • On CRAN (26): 'test-autoplot.R:33:3', 'test-autoplot.R:125:1', 'test-buffer.R:10:3', 'test-buffer.R:168:1', 'test-buffer.R:205:3', 'test-buffer.R:291:3', 'test-compat-vctrs.R:128:1', 'test-misc.R:1:1', 'test-misc.R:20:1', 'test-misc.R:31:1', 'test-spatial_clustering_cv.R:70:1', 'test-spatial_clustering_cv.R:110:1', 'test-spatial_clustering_cv.R:217:1', 'test-spatial_nndm_cv.R:13:1', 'test-spatial_nndm_cv.R:37:1', 'test-spatial_nndm_cv.R:76:1', 'test-spatial_nndm_cv.R:101:1', 'test-spatial_vfold_cv.R:10:1', 'test-spatial_vfold_cv.R:24:1', 'test-spatial_vfold_cv.R:84:1', 'test-spatial_vfold_cv.R:225:1', 'test-spatial_block_cv.R:10:1', 'test-spatial_block_cv.R:68:1', 'test-spatial_block_cv.R:225:1', 'test-spatial_block_cv.R:261:1', 'test-spatial_block_cv.R:321:1' ══ Failed tests ════════════════════════════════════════════════════════════════ ── Error ('test-spatial_vfold_cv.R:200:3'): bad args ─────────────────────────── Error in `rsample::vfold_cv(data = data, v = v, repeats = repeats, strata = { { strata } }, breaks = breaks, pool = pool, ...)`: Leave-one-out cross-validation is not supported by this function. x You set `v` to `nrow(data)`, which would result in a leave-one-out cross-validation. i Use `loo_cv()` in this case. Backtrace: ▆ 1. └─spatialsample::spatial_buffer_vfold_cv(...) 2. └─rsample::vfold_cv(...) 3. └─rsample:::vfold_splits(...) 4. └─rsample:::check_v(v, n, prevent_loo = prevent_loo, call = rlang::caller_env()) 5. └─cli::cli_abort(...) 6. └─rlang::abort(...) [ FAIL 1 | WARN 0 | SKIP 26 | PASS 535 ] Deleting unused snapshots: 'autoplot/buffered-llo-set-plot.svg', 'autoplot/buffered-llo-split-plot.svg', 'autoplot/buffered-rsample-plot.svg', 'autoplot/buffered-rset-plot.svg', 'autoplot/buffered-vfold-plot.svg', 'autoplot/buffered-vfold-split.svg', 'autoplot/repeated-block-cv.svg', 'autoplot/repeated-llo.svg', 'autoplot/repeated-vfold.svg', and 'autoplot/snake-flips-rows-the-right-way.svg' Error: ! Test failures. Execution halted Package: ssdtools Check: tests New result: ERROR Running ‘testthat.R’ [173s/174s] Running the tests in ‘tests/testthat.R’ failed. Complete output: > # Copyright 2015-2023 Province of British Columbia > # Copyright 2021 Environment and Climate Change Canada > # Copyright 2023-2024 Australian Government Department of Climate Change, > # Energy, the Environment and Water > # > # Licensed under the Apache License, Version 2.0 (the "License"); > # you may not use this file except in compliance with the License. > # You may obtain a copy of the License at > # > # https://www.apache.org/licenses/LICENSE-2.0 > # > # Unless required by applicable law or agreed to in writing, software > # distributed under the License is distributed on an "AS IS" BASIS, > # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. > # See the License for the specific language governing permissions and > # limitations under the License. > > library(testthat) > library(ssdtools) > > test_check("ssdtools") Saving _problems/test-exposure-46.R Saving _problems/test-hc-653.R [ FAIL 2 | WARN 0 | SKIP 214 | PASS 989 ] ══ Skipped tests (214) ═════════════════════════════════════════════════════════ • On CRAN (180): 'test-at-boundary.R:16:1', 'test-at-boundary.R:35:1', 'test-at-boundary.R:54:1', 'test-bcanz.R:28:1', 'test-bcanz.R:41:1', 'test-burrIII3.R:18:1', 'test-burrIII3.R:27:1', 'test-burrIII3.R:36:1', 'test-burrIII3.R:46:1', 'test-censor.R:43:1', 'test-censor.R:47:1', 'test-censor.R:51:1', 'test-censoring.R:1:1', 'test-censoring.R:26:1', 'test-censoring.R:50:1', 'test-ci-methods.R:1:1', 'test-coef.R:18:1', 'test-data.R:18:1', 'test-data.R:26:1', 'test-est-methods.R:1:1', 'test-estimates.R:18:1', 'test-estimates.R:26:1', 'test-exposure.R:18:1', 'test-exposure.R:26:1', 'test-exposure.R:34:1', 'test-exposure.R:50:1', 'test-fit.R:238:1', 'test-fit.R:265:1', 'test-fit.R:279:1', 'test-fit.R:290:1', 'test-fit.R:319:1', 'test-fit.R:347:1', 'test-fit.R:388:1', 'test-fit.R:394:1', 'test-fit.R:402:1', 'test-fit.R:411:1', 'test-gamma.R:17:1', 'test-glance.R:18:1', 'test-glance.R:28:1', 'test-glance.R:38:1', 'test-glance.R:79:1', 'test-gof.R:18:1', 'test-gof.R:28:1', 'test-gof.R:37:1', 'test-gof.R:46:1', 'test-gof.R:60:1', 'test-gof.R:74:1', 'test-gof.R:88:1', 'test-gompertz.R:18:1', 'test-hc-burrlioz.R:25:1', 'test-hc-burrlioz.R:33:1', 'test-hc-burrlioz.R:41:1', 'test-hc-burrlioz.R:65:1', 'test-hc-root.R:18:1', 'test-hc-root.R:30:1', 'test-hc.R:18:1', 'test-hc.R:26:1', 'test-hc.R:33:1', 'test-hc.R:42:1', 'test-hc.R:51:1', 'test-hc.R:160:1', 'test-hc.R:168:1', 'test-hc.R:176:1', 'test-hc.R:184:1', 'test-hc.R:192:1', 'test-hc.R:200:1', 'test-hc.R:210:1', 'test-hc.R:220:1', 'test-hc.R:227:1', 'test-hc.R:234:1', 'test-hc.R:246:1', 'test-hc.R:254:1', 'test-hc.R:262:1', 'test-hc.R:277:1', 'test-hc.R:285:1', 'test-hc.R:293:1', 'test-hc.R:314:1', 'test-hc.R:328:1', 'test-hc.R:343:1', 'test-hc.R:359:1', 'test-hc.R:415:1', 'test-hc.R:425:1', 'test-hc.R:461:1', 'test-hc.R:479:1', 'test-hc.R:492:1', 'test-hc.R:500:1', 'test-hc.R:512:1', 'test-hc.R:520:1', 'test-hc.R:532:1', 'test-hc.R:547:1', 'test-hc.R:574:1', 'test-hc.R:592:1', 'test-hc.R:620:1', 'test-hc.R:657:1', 'test-hc.R:679:1', 'test-hc.R:693:1', 'test-hc.R:739:1', 'test-hc.R:767:1', 'test-hc.R:849:1', 'test-hp-burrlioz.R:18:1', 'test-hp-burrlioz.R:26:1', 'test-hp-burrlioz.R:34:1', 'test-hp-burrlioz.R:58:1', 'test-hp-root.R:18:1', 'test-hp-root.R:37:1', 'test-hp-root.R:52:1', 'test-hp.R:18:1', 'test-hp.R:27:1', 'test-hp.R:52:1', 'test-hp.R:60:1', 'test-hp.R:68:1', 'test-hp.R:76:1', 'test-hp.R:84:1', 'test-hp.R:92:1', 'test-hp.R:100:1', 'test-hp.R:108:1', 'test-hp.R:118:1', 'test-hp.R:126:1', 'test-hp.R:146:1', 'test-hp.R:160:1', 'test-hp.R:172:1', 'test-hp.R:179:1', 'test-hp.R:194:1', 'test-hp.R:202:1', 'test-hp.R:239:1', 'test-hp.R:249:1', 'test-hp.R:285:1', 'test-hp.R:303:1', 'test-hp.R:318:1', 'test-hp.R:326:1', 'test-hp.R:361:1', 'test-invpareto.R:18:1', 'test-invpareto.R:27:1', 'test-invpareto.R:34:1', 'test-invpareto.R:43:1', 'test-invpareto.R:52:1', 'test-invpareto.R:64:1', 'test-invpareto.R:73:1', 'test-invpareto.R:82:1', 'test-invpareto.R:91:1', 'test-lgumbel.R:18:1', 'test-llogis-llogis.R:22:1', 'test-llogis.R:18:1', 'test-lnorm-lnorm.R:18:1', 'test-lnorm-lnorm.R:51:1', 'test-lnorm-lnorm.R:58:1', 'test-lnorm.R:18:1', 'test-multi.R:18:1', 'test-multi.R:46:1', 'test-multi.R:62:1', 'test-multi.R:81:1', 'test-multi.R:97:1', 'test-multi.R:116:1', 'test-multi.R:136:1', 'test-multi.R:158:1', 'test-operators.R:1:1', 'test-operators.R:16:1', 'test-operators.R:28:1', 'test-predict.R:18:1', 'test-predict.R:26:1', 'test-predict.R:36:1', 'test-predict.R:46:1', 'test-predict.R:58:1', 'test-print.R:18:1', 'test-print.R:23:1', 'test-print.R:28:1', 'test-print.R:37:1', 'test-print.R:46:1', 'test-print.R:55:1', 'test-schwarz-tillmans.R:18:1', 'test-summary.R:18:1', 'test-tidy.R:18:1', 'test-utils.R:36:1', 'test-weibull.R:18:1', 'test-weibull.R:27:1', 'test-weibull.R:36:1', 'test-weibull.R:44:1', 'test-weibull.R:52:1', 'test-weighted.R:44:1', 'test-weighted.R:79:1' • On Linux (34): 'test-autoplot.R:20:3', 'test-autoplot.R:25:3', 'test-autoplot.R:33:3', 'test-autoplot.R:42:3', 'test-ggplot.R:59:3', 'test-ggplot.R:67:3', 'test-ggplot.R:73:3', 'test-ggplot.R:84:3', 'test-ggplot.R:90:3', 'test-ggplot.R:96:3', 'test-ggplot.R:102:3', 'test-ggplot.R:110:3', 'test-ggplot.R:118:3', 'test-match-moments.R:24:3', 'test-plot-cdf.R:21:3', 'test-plot-cdf.R:28:3', 'test-plot-cdf.R:32:3', 'test-plot-cdf.R:46:3', 'test-plot-cdf.R:54:3', 'test-plot-data.R:19:3', 'test-plot-data.R:23:3', 'test-plot-data.R:32:3', 'test-ssd-plot.R:19:3', 'test-ssd-plot.R:29:3', 'test-ssd-plot.R:35:3', 'test-ssd-plot.R:40:3', 'test-ssd-plot.R:50:3', 'test-ssd-plot.R:66:3', 'test-ssd-plot.R:73:3', 'test-ssd-plot.R:77:3', 'test-ssd-plot.R:81:3', 'test-ssd-plot.R:85:3', 'test-ssd-plot.R:89:3', 'test-ssd-plot.R:93:3' ══ Failed tests ════════════════════════════════════════════════════════════════ ── Error ('test-exposure.R:46:5'): exposure multiple distributions ───────────── Error in `expect_snapshot_value(ssd_exposure(fits, nboot = 100), 0.0663472624824284, style = "deparse")`: `cran` must be `TRUE` or `FALSE`, not the number 0.07. Backtrace: ▆ 1. ├─withr::with_seed(...) at test-exposure.R:45:3 2. │ └─withr::with_preserve_seed(...) 3. └─testthat::expect_snapshot_value(cran = 0.0663472624824284) at test-exposure.R:46:5 4. └─testthat:::check_bool(cran) 5. └─testthat:::stop_input_type(...) 6. └─rlang::abort(message, ..., call = call, arg = arg) ── Error ('test-hc.R:653:3'): ssd_hc save_to lnorm 1 ─────────────────────────── Error in `expect_snapshot_value(hc$lcl, est, style = "deparse")`: `cran` must be `TRUE` or `FALSE`, not the number 1.29. Backtrace: ▆ 1. └─testthat::expect_snapshot_value(cran = est) at test-hc.R:653:3 2. └─testthat:::check_bool(cran) 3. └─testthat:::stop_input_type(...) 4. └─rlang::abort(message, ..., call = call, arg = arg) [ FAIL 2 | WARN 0 | SKIP 214 | PASS 989 ] Deleting unused snapshots: 'autoplot/autoplot.png', 'autoplot/autoplot_bigmark.png', 'autoplot/autoplot_new.png', 'autoplot/autoplot_rescale.png', 'autoplot/suffix.png', 'ggplot/geom_hcintersect.png', 'ggplot/geom_hcintersect_aes.png', 'ggplot/geom_ssdpoint.png', 'ggplot/geom_ssdpoint_identity.png', 'ggplot/geom_ssdsegment.png', 'ggplot/geom_ssdsegment_arrow.png', 'ggplot/geom_ssdsegment_identity.png', 'ggplot/geom_ssdsegment_nodata.png', 'ggplot/geom_xribbon.png', 'match-moments/cdf.png', 'plot-cdf/fits.png', 'plot-cdf/fits_average.png', 'plot-cdf/fits_average_na.png', …, 'ssd-plot/ribbon.png', and 'ssd-plot/suffix.png' Error: ! Test failures. Execution halted Package: testdat Check: tests New result: ERROR Running ‘testthat.R’ [11s/11s] Running the tests in ‘tests/testthat.R’ failed. Complete output: > library(testthat) > library(testdat) > > test_check("testdat") Saving _problems/test-expect-labels-17.R Saving _problems/test-expect-labels-24.R Saving _problems/test-expect-labels-32.R Saving _problems/test-expect-labels-39.R Saving _problems/test-expect-labels-49.R Saving _problems/test-expect-labels-56.R Saving _problems/test-expect-labels-62.R Saving _problems/test-expect-labels-68.R Saving _problems/test-expect-labels-76.R Saving _problems/test-expect-labels-82.R Saving _problems/test-expect-labels-88.R Saving _problems/test-expect-labels-98.R Saving _problems/test-expect-labels-106.R Saving _problems/test-expect-labels-112.R Saving _problems/test-expect-labels-118.R Saving _problems/test-expect-labels-124.R Saving _problems/test-expect-proportion-9.R Saving _problems/test-expect-proportion-10.R Saving _problems/test-expect-proportion-16.R Saving _problems/test-expect-proportion-17.R Saving _problems/test-expect-proportion-27.R Saving _problems/test-expect-proportion-28.R Saving _problems/test-expect-proportion-29.R Saving _problems/test-expect-proportion-41.R Saving _problems/test-expect-proportion-41.R Saving _problems/test-expect-proportion-43.R Saving _problems/test-expect-proportion-41.R Saving _problems/test-expect-proportion-43.R Saving _problems/test-expect-proportion-41.R Saving _problems/test-expect-proportion-41.R Saving _problems/test-expect-proportion-41.R Saving _problems/test-expect-proportion-41.R Saving _problems/test-expect-proportion-41.R Saving _problems/test-expect-unique-14.R Saving _problems/test-expect-unique-15.R Saving _problems/test-expect-unique-26.R Saving _problems/test-expect-unique-27.R Saving _problems/test-expect-unique-28.R Saving _problems/test-expect-unique-40.R Saving _problems/test-expect-unique-41.R Saving _problems/test-expect-unique-44.R Saving _problems/test-expect-unique-45.R Saving _problems/test-expect-unique-48.R Saving _problems/test-expect-unique-49.R Saving _problems/test-expect-unique-58.R Saving _problems/test-expect-unique-59.R Saving _problems/test-expect-value-7.R Saving _problems/test-expect-value-8.R Saving _problems/test-expect-value-9.R Saving _problems/test-expect_base-2.R Saving _problems/test-expect_base-3.R Saving _problems/test-expect_base-7.R Saving _problems/test-expect_base-8.R Saving _problems/test-expect_datacomp-5.R Saving _problems/test-expect_datacomp-6.R Saving _problems/test-expect_datacomp-23.R Saving _problems/test-expect_datacomp-25.R Saving _problems/test-expect_datacomp-27.R Saving _problems/test-expect_datacomp-29.R Saving _problems/test-expect_depends-7.R Saving _problems/test-expect_depends-14.R Saving _problems/test-expect_depends-22.R Saving _problems/test-expect_depends-30.R Saving _problems/test-expect_depends-39.R Saving _problems/test-expect_depends-48.R Saving _problems/test-expect_exclusive-19.R Saving _problems/test-expect_exclusive-20.R Saving _problems/test-expect_generic-3.R Saving _problems/test-expect_generic-4.R Saving _problems/test-expect_generic-6.R Saving _problems/test-expect_generic-7.R Saving _problems/test-expect_make-10.R Saving _problems/test-expect_make-11.R Saving _problems/test-expect_make-12.R Saving _problems/test-expect_make-13.R Saving _problems/test-expect_make-26.R Saving _problems/test-expect_make-27.R Saving _problems/test-expect_make-28.R Saving _problems/test-expect_make-29.R Saving _problems/test-expect_make-31.R Saving _problems/test-expect_make-32.R Saving _problems/test-expect_range-8.R Saving _problems/test-expect_range-9.R Saving _problems/test-expect_range-10.R Saving _problems/test-expect_range-14.R Saving _problems/test-expect_range-15.R Saving _problems/test-expect_range-19.R Saving _problems/test-expect_range-20.R Saving _problems/test-expect_range-21.R Saving _problems/test-expect_range-22.R Saving _problems/test-reporter_excel-8.R Saving _problems/test-reporter_excel-9.R Saving _problems/test-reporter_excel-46.R Saving _problems/test-reporter_excel-47.R Saving _problems/test-testdata-9.R [ FAIL 95 | WARN 0 | SKIP 0 | PASS 107 ] ══ Failed tests ════════════════════════════════════════════════════════════════ ── Failure ('test-expect-labels.R:12:3'): Basic use ──────────────────────────── Expected exactly one success and no failures. Actually succeeded 2 times ── Failure ('test-expect-labels.R:19:3'): Basic use ──────────────────────────── Expected exactly one success and no failures. Actually succeeded 2 times ── Failure ('test-expect-labels.R:27:3'): Basic use ──────────────────────────── Expected exactly one failure and no successes. Actually failed 2 times ── Failure ('test-expect-labels.R:34:3'): Basic use ──────────────────────────── Expected exactly one failure and no successes. Actually failed 2 times ── Failure ('test-expect-labels.R:44:3'): Weak label checks work ─────────────── Expected exactly one success and no failures. Actually succeeded 2 times ── Failure ('test-expect-labels.R:51:3'): Weak label checks work ─────────────── Expected exactly one success and no failures. Actually succeeded 2 times ── Failure ('test-expect-labels.R:58:3'): Weak label checks work ─────────────── Expected exactly one success and no failures. Actually succeeded 2 times ── Failure ('test-expect-labels.R:64:3'): Weak label checks work ─────────────── Expected exactly one success and no failures. Actually succeeded 2 times ── Failure ('test-expect-labels.R:71:3'): Weak label checks work ─────────────── Expected exactly one failure and no successes. Actually failed 2 times ── Failure ('test-expect-labels.R:78:3'): Weak label checks work ─────────────── Expected exactly one failure and no successes. Actually failed 2 times ── Failure ('test-expect-labels.R:84:3'): Weak label checks work ─────────────── Expected exactly one failure and no successes. Actually failed 2 times ── Failure ('test-expect-labels.R:93:3'): All records fail where variable labels don't match ── Expected exactly one failure and no successes. Actually failed 2 times ── Failure ('test-expect-labels.R:102:3'): Test only fails when there is a label discrepancy for values present in the data ── Expected exactly one success and no failures. Actually succeeded 2 times ── Failure ('test-expect-labels.R:108:3'): Test only fails when there is a label discrepancy for values present in the data ── Expected exactly one success and no failures. Actually succeeded 2 times ── Failure ('test-expect-labels.R:114:3'): Test only fails when there is a label discrepancy for values present in the data ── Expected exactly one success and no failures. Actually succeeded 2 times ── Failure ('test-expect-labels.R:120:3'): Test only fails when there is a label discrepancy for values present in the data ── Expected exactly one failure and no successes. Actually failed 2 times ── Failure ('test-expect-proportion.R:9:3'): prop_gte ────────────────────────── Expected exactly one success and no failures. Actually succeeded 2 times ── Failure ('test-expect-proportion.R:10:3'): prop_gte ───────────────────────── Expected exactly one failure and no successes. Actually failed 2 times ── Failure ('test-expect-proportion.R:16:3'): prop_gte ───────────────────────── Expected exactly one success and no failures. Actually succeeded 2 times ── Failure ('test-expect-proportion.R:17:3'): prop_gte ───────────────────────── Expected exactly one failure and no successes. Actually failed 2 times ── Failure ('test-expect-proportion.R:27:3'): proportion missing ─────────────── Expected exactly one success and no failures. Actually succeeded 2 times ── Failure ('test-expect-proportion.R:28:3'): proportion missing ─────────────── Expected exactly one failure and no successes. Actually failed 2 times ── Failure ('test-expect-proportion.R:29:3'): proportion missing ─────────────── Expected exactly one failure and no successes. Actually failed 2 times ── Failure ('test-expect-proportion.R:41:7'): proportion valid values ────────── Expected exactly one success and no failures. Actually succeeded 2 times ── Failure ('test-expect-proportion.R:41:7'): proportion valid values ────────── Expected exactly one success and no failures. Actually succeeded 2 times ── Failure ('test-expect-proportion.R:43:7'): proportion valid values ────────── Expected exactly one failure and no successes. Actually failed 2 times ── Failure ('test-expect-proportion.R:41:7'): proportion valid values ────────── Expected exactly one success and no failures. Actually succeeded 2 times ── Failure ('test-expect-proportion.R:43:7'): proportion valid values ────────── Expected exactly one failure and no successes. Actually failed 2 times ── Failure ('test-expect-proportion.R:41:7'): proportion valid values ────────── Expected exactly one success and no failures. Actually succeeded 2 times ── Failure ('test-expect-proportion.R:41:7'): proportion valid values ────────── Expected exactly one success and no failures. Actually succeeded 2 times ── Failure ('test-expect-proportion.R:41:7'): proportion valid values ────────── Expected exactly one success and no failures. Actually succeeded 2 times ── Failure ('test-expect-proportion.R:41:7'): proportion valid values ────────── Expected exactly one success and no failures. Actually succeeded 2 times ── Failure ('test-expect-proportion.R:41:7'): proportion valid values ────────── Expected exactly one success and no failures. Actually succeeded 2 times ── Failure ('test-expect-unique.R:14:3'): expect_unique_across ───────────────── Expected exactly one success and no failures. Actually succeeded 2 times ── Failure ('test-expect-unique.R:15:3'): expect_unique_across ───────────────── Expected exactly one failure and no successes. Actually failed 2 times ── Failure ('test-expect-unique.R:26:3'): expect_unique_combine ──────────────── Expected exactly one success and no failures. Actually succeeded 2 times ── Failure ('test-expect-unique.R:27:3'): expect_unique_combine ──────────────── Expected exactly one failure and no successes. Actually failed 2 times ── Failure ('test-expect-unique.R:28:3'): expect_unique_combine ──────────────── Expected exactly one failure and no successes. Actually failed 2 times ── Failure ('test-expect-unique.R:40:3'): exclude argument works as expected ─── Expected exactly one success and no failures. Actually succeeded 2 times ── Failure ('test-expect-unique.R:41:3'): exclude argument works as expected ─── Expected exactly one failure and no successes. Actually failed 2 times ── Failure ('test-expect-unique.R:44:3'): exclude argument works as expected ─── Expected exactly one success and no failures. Actually succeeded 2 times ── Failure ('test-expect-unique.R:45:3'): exclude argument works as expected ─── Expected exactly one failure and no successes. Actually failed 2 times ── Failure ('test-expect-unique.R:48:3'): exclude argument works as expected ─── Expected exactly one success and no failures. Actually succeeded 2 times ── Failure ('test-expect-unique.R:49:3'): exclude argument works as expected ─── Expected exactly one failure and no successes. Actually failed 2 times ── Failure ('test-expect-unique.R:58:3'): exclude argument works correctly with multiple vars in expect_unique() ── Expected exactly one failure and no successes. Actually failed 2 times ── Failure ('test-expect-unique.R:59:3'): exclude argument works correctly with multiple vars in expect_unique() ── Expected exactly one success and no failures. Actually succeeded 2 times ── Failure ('test-expect-value.R:7:3'): expect_values ────────────────────────── Expected exactly one success and no failures. Actually succeeded 2 times ── Failure ('test-expect-value.R:8:3'): expect_values ────────────────────────── Expected exactly one failure and no successes. Actually failed 2 times ── Failure ('test-expect-value.R:9:3'): expect_values ────────────────────────── Expected exactly one success and no failures. Actually succeeded 2 times ── Failure ('test-expect_base.R:2:3'): basic examples ────────────────────────── Expected exactly one success and no failures. Actually succeeded 2 times ── Failure ('test-expect_base.R:3:3'): basic examples ────────────────────────── Expected exactly one failure and no successes. Actually failed 2 times ── Failure ('test-expect_base.R:7:3'): NA check results are handled correctly ── Expected exactly one success and no failures. Actually succeeded 2 times ── Failure ('test-expect_base.R:8:3'): NA check results are handled correctly ── Expected exactly one failure and no successes. Actually failed 2 times ── Failure ('test-expect_datacomp.R:5:3'): expect_valmatch ───────────────────── Expected exactly one success and no failures. Actually succeeded 2 times ── Failure ('test-expect_datacomp.R:6:3'): expect_valmatch ───────────────────── Expected exactly one failure and no successes. Actually failed 2 times ── Failure ('test-expect_datacomp.R:23:3'): expect_subset ────────────────────── Expected exactly one success and no failures. Actually succeeded 2 times ── Failure ('test-expect_datacomp.R:25:3'): expect_subset ────────────────────── Expected exactly one success and no failures. Actually succeeded 2 times ── Failure ('test-expect_datacomp.R:27:3'): expect_subset ────────────────────── Expected exactly one failure and no successes. Actually failed 2 times ── Failure ('test-expect_datacomp.R:29:3'): expect_subset ────────────────────── Expected exactly one failure and no successes. Actually failed 2 times ── Failure ('test-expect_depends.R:7:3'): expect_depends works ───────────────── Expected exactly one success and no failures. Actually succeeded 2 times ── Failure ('test-expect_depends.R:14:3'): expect_depends works ──────────────── Expected exactly one failure and no successes. Actually failed 2 times ── Failure ('test-expect_depends.R:22:3'): expect_depends works ──────────────── Expected exactly one success and no failures. Actually succeeded 2 times ── Failure ('test-expect_depends.R:30:3'): expect_depends works ──────────────── Expected exactly one failure and no successes. Actually failed 2 times ── Failure ('test-expect_depends.R:39:3'): expect_depends works ──────────────── Expected exactly one success and no failures. Actually succeeded 2 times ── Failure ('test-expect_depends.R:48:3'): expect_depends works ──────────────── Expected exactly one failure and no successes. Actually failed 2 times ── Failure ('test-expect_exclusive.R:19:3'): Exclusive check works ───────────── Expected exactly one failure and no successes. Actually failed 2 times ── Failure ('test-expect_exclusive.R:20:3'): Exclusive check works ───────────── Expected exactly one success and no failures. Actually succeeded 2 times ── Failure ('test-expect_generic.R:3:3'): basic examples ─────────────────────── Expected exactly one success and no failures. Actually succeeded 2 times ── Failure ('test-expect_generic.R:4:3'): basic examples ─────────────────────── Expected exactly one failure and no successes. Actually failed 2 times ── Failure ('test-expect_generic.R:6:3'): basic examples ─────────────────────── Expected exactly one success and no failures. Actually succeeded 2 times ── Failure ('test-expect_generic.R:7:3'): basic examples ─────────────────────── Expected exactly one failure and no successes. Actually failed 2 times ── Failure ('test-expect_make.R:10:3'): basic examples ───────────────────────── Expected exactly one success and no failures. Actually succeeded 2 times ── Failure ('test-expect_make.R:11:3'): basic examples ───────────────────────── Expected exactly one success and no failures. Actually succeeded 2 times ── Failure ('test-expect_make.R:12:3'): basic examples ───────────────────────── Expected exactly one failure and no successes. Actually failed 2 times ── Failure ('test-expect_make.R:13:3'): basic examples ───────────────────────── Expected exactly one failure and no successes. Actually failed 2 times ── Failure ('test-expect_make.R:26:3'): automatically generated expectations ─── Expected exactly one failure and no successes. Actually failed 2 times ── Failure ('test-expect_make.R:27:3'): automatically generated expectations ─── Expected exactly one failure and no successes. Actually failed 2 times ── Failure ('test-expect_make.R:28:3'): automatically generated expectations ─── Expected exactly one failure and no successes. Actually failed 2 times ── Failure ('test-expect_make.R:29:3'): automatically generated expectations ─── Expected exactly one failure and no successes. Actually failed 2 times ── Failure ('test-expect_make.R:31:3'): automatically generated expectations ─── Expected exactly one success and no failures. Actually succeeded 2 times ── Failure ('test-expect_make.R:32:3'): automatically generated expectations ─── Expected exactly one success and no failures. Actually succeeded 2 times ── Failure ('test-expect_range.R:8:3'): min max works with only min and max provided ── Expected exactly one success and no failures. Actually succeeded 2 times ── Failure ('test-expect_range.R:9:3'): min max works with only min and max provided ── Expected exactly one failure and no successes. Actually failed 2 times ── Failure ('test-expect_range.R:10:3'): min max works with only min and max provided ── Expected exactly one failure and no successes. Actually failed 2 times ── Failure ('test-expect_range.R:14:3'): min max works with real numbers ─────── Expected exactly one success and no failures. Actually succeeded 2 times ── Failure ('test-expect_range.R:15:3'): min max works with real numbers ─────── Expected exactly one failure and no successes. Actually failed 2 times ── Failure ('test-expect_range.R:19:3'): min max works additional valid values provided ── Expected exactly one success and no failures. Actually succeeded 2 times ── Failure ('test-expect_range.R:20:3'): min max works additional valid values provided ── Expected exactly one success and no failures. Actually succeeded 2 times ── Failure ('test-expect_range.R:21:3'): min max works additional valid values provided ── Expected exactly one failure and no successes. Actually failed 2 times ── Failure ('test-expect_range.R:22:3'): min max works additional valid values provided ── Expected exactly one failure and no successes. Actually failed 2 times ── Failure ('test-reporter_excel.R:8:3'): excel_results ──────────────────────── Expected exactly one success and no failures. Actually succeeded 2 times ── Failure ('test-reporter_excel.R:9:3'): excel_results ──────────────────────── Expected exactly one success and no failures. Actually succeeded 2 times ── Failure ('test-reporter_excel.R:46:3'): excel_results ─────────────────────── Expected `x_xl_summary` to equal `xl_summary`. Differences: Component "tests": Mean relative difference: 0.5 Component "failed": Mean relative difference: 0.5 ── Failure ('test-reporter_excel.R:47:3'): excel_results ─────────────────────── Expected `x_xl_failing` to equal `xl_failing`. Differences: Attributes: < Component "row.names": Numeric: lengths (4, 2) differ > Component "context": Lengths (4, 2) differ (string compare on first 2) Component "test": Lengths (4, 2) differ (string compare on first 2) Component "status": Lengths (4, 2) differ (string compare on first 2) Component "variable": Lengths (4, 2) differ (string compare on first 2) Component "variable": 'is.NA' value mismatch: 0 in current 1 in target Component "description": Lengths (4, 2) differ (string compare on first 2) Component "description": 1 string mismatch Component "failed_records": Numeric: lengths (4, 2) differ ... ── Failure ('test-testdata.R:9:3'): set_testdata/get_testdata work correctly ─── Expected exactly one failure and no successes. Actually failed 2 times [ FAIL 95 | WARN 0 | SKIP 0 | PASS 107 ] Error: ! Test failures. Execution halted Package: testex Check: tests New result: ERROR Running ‘spelling.R’ [0s/0s] Running ‘testthat.R’ [4s/4s] Running the tests in ‘tests/testthat.R’ failed. Complete output: > library(testthat) > library(testex) > > test_check("testex") [ FAIL 1 | WARN 0 | SKIP 2 | PASS 80 ] ══ Skipped tests (2) ═══════════════════════════════════════════════════════════ • on R CMD check (1): 'test-options.R:27:3' • tests not installed (1): 'test-srcref-key.R:27:3' ══ Failed tests ════════════════════════════════════════════════════════════════ ── Error ('test-pkgexample.R:15:5'): test_examples_as_testthat converts examples to tests and executes test suite ── Error: ! in callr subprocess. Caused by error in `reporter$add_result(context = reporter$.context, test = test, …`: ! attempt to use zero-length variable name Backtrace: ▆ 1. ├─testthat::expect_silent(...) at test-pkgexample.R:14:3 2. │ └─testthat:::quasi_capture(enquo(object), NULL, evaluate_promise) 3. │ ├─testthat (local) .capture(...) 4. │ │ ├─withr::with_output_sink(...) 5. │ │ │ └─base::force(code) 6. │ │ ├─base::withCallingHandlers(...) 7. │ │ └─base::withVisible(code) 8. │ └─rlang::eval_bare(quo_get_expr(.quo), quo_get_env(.quo)) 9. └─callr::r(...) at test-pkgexample.R:15:5 10. └─callr:::get_result(output = out, options) 11. └─throw(callr_remote_error(remerr, output), parent = fix_msg(remerr[[3]])) [ FAIL 1 | WARN 0 | SKIP 2 | PASS 80 ] Error: ! Test failures. Execution halted Package: texreg Check: tests New result: ERROR Running ‘testthat.R’ [10s/10s] Running the tests in ‘tests/testthat.R’ failed. Complete output: > library("testthat") > library("texreg") Version: 1.39.4 Date: 2024-07-23 Author: Philip Leifeld (University of Manchester) Consider submitting praise using the praise or praise_interactive functions. Please cite the JSS article in your publications -- see citation("texreg"). > > test_check("texreg") Saving _problems/test-huxtablereg-15.R Saving _problems/test-texreg-323.R [ FAIL 2 | WARN 1 | SKIP 33 | PASS 201 ] ══ Skipped tests (33) ══════════════════════════════════════════════════════════ • On CRAN (32): 'test-extract.R:6:3', 'test-extract.R:28:3', 'test-extract.R:67:3', 'test-extract.R:90:3', 'test-extract.R:162:3', 'test-extract.R:193:3', 'test-extract.R:214:3', 'test-extract.R:239:3', 'test-extract.R:280:3', 'test-extract.R:305:3', 'test-extract.R:328:3', 'test-extract.R:367:3', 'test-extract.R:415:3', 'test-extract.R:441:3', 'test-extract.R:467:3', 'test-extract.R:501:3', 'test-extract.R:543:3', 'test-extract.R:591:3', 'test-extract.R:615:3', 'test-extract.R:651:3', 'test-extract.R:738:3', 'test-extract.R:761:3', 'test-extract.R:795:3', 'test-extract.R:824:3', 'test-extract.R:859:3', 'test-extract.R:926:3', 'test-extract.R:979:3', 'test-extract.R:1001:3', 'test-extract.R:1030:3', 'test-extract.R:1054:3', 'test-extract.R:1086:3', 'test-extract.R:1178:3' • {biglm} is not installed (1): 'test-plotreg.R:39:3' ══ Failed tests ════════════════════════════════════════════════════════════════ ── Error ('test-huxtablereg.R:13:3'): huxtablereg gives useful error message if huxtable not installed ── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test-huxtablereg.R:13:3 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) ── Error ('test-texreg.R:319:3'): knitreg function works ─────────────────────── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test-texreg.R:319:3 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) [ FAIL 2 | WARN 1 | SKIP 33 | PASS 201 ] Error: ! Test failures. Execution halted Package: ThankYouStars Check: tests New result: ERROR Running ‘testthat.R’ [1s/1s] Running the tests in ‘tests/testthat.R’ failed. Complete output: > library(testthat) > library(ThankYouStars) > > test_check("ThankYouStars") Saving _problems/test-starring-12.R [ FAIL 1 | WARN 0 | SKIP 0 | PASS 0 ] ══ Failed tests ════════════════════════════════════════════════════════════════ ── Error ('test-starring.R:3:1'): (code run outside of `test_that()`) ────────── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test-starring.R:3:1 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) [ FAIL 1 | WARN 0 | SKIP 0 | PASS 0 ] Error: ! Test failures. Execution halted Package: tibblify Check: tests New result: ERROR Running ‘spelling.R’ [0s/0s] Running ‘testthat.R’ [35s/35s] Running the tests in ‘tests/testthat.R’ failed. Complete output: > library(testthat) > library(tibblify) > > test_check("tibblify") [ FAIL 1 | WARN 0 | SKIP 85 | PASS 541 ] ══ Skipped tests (85) ══════════════════════════════════════════════════════════ • On CRAN (84): 'test-format.R:1:1', 'test-format.R:7:1', 'test-format.R:46:1', 'test-format.R:66:1', 'test-format.R:102:1', 'test-format.R:114:1', 'test-format.R:178:1', 'test-format.R:184:1', 'test-format.R:213:1', 'test-format.R:237:1', 'test-format.R:244:1', 'test-format.R:258:1', 'test-format.R:322:1', 'test-nest-tree.R:1:1', 'test-nest-tree.R:25:1', 'test-nest-tree.R:49:1', 'test-nest-tree.R:61:1', 'test-nest-tree.R:74:1', 'test-nest-tree.R:86:1', 'test-nest-tree.R:98:1', 'test-parse-open-api.R:2:3', 'test-spec_combine.R:1:1', 'test-spec_combine.R:13:1', 'test-spec_combine.R:19:1', 'test-spec_combine.R:63:1', 'test-spec_combine.R:90:1', 'test-spec_combine.R:104:1', 'test-spec_combine.R:128:1', 'test-spec_combine.R:141:1', 'test-spec_combine.R:158:1', 'test-spec_guess.R:1:1', 'test-spec_guess.R:7:1', 'test-spec_guess.R:11:1', 'test-spec_guess.R:15:1', 'test-spec_guess.R:19:1', 'test-spec_guess.R:32:1', 'test-spec_guess.R:58:1', 'test-spec_guess.R:63:1', 'test-spec_guess.R:70:1', 'test-spec_guess_df.R:436:1', 'test-spec_guess_df.R:443:1', 'test-spec_guess_object.R:308:1', 'test-spec_inform_unspecified.R:1:1', 'test-tib_spec.R:4:1', 'test-tib_spec.R:11:1', 'test-tib_spec.R:18:1', 'test-tib_spec.R:41:1', 'test-tib_spec.R:64:1', 'test-tib_spec.R:71:1', 'test-tib_spec.R:75:1', 'test-tib_spec.R:81:1', 'test-tib_spec.R:91:1', 'test-tib_spec.R:104:1', 'test-tib_spec.R:114:1', 'test-tib_spec.R:129:1', 'test-tib_spec.R:159:1', 'test-tib_spec.R:225:1', 'test-tibblify.R:1:1', 'test-tibblify.R:11:1', 'test-tibblify.R:51:1', 'test-tibblify.R:111:3', 'test-tibblify.R:192:1', 'test-tibblify.R:284:1', 'test-tibblify.R:328:1', 'test-tibblify.R:375:1', 'test-tibblify.R:451:1', 'test-tibblify.R:523:1', 'test-tibblify.R:573:1', 'test-tibblify.R:852:3', 'test-tibblify.R:1138:1', 'test-tibblify.R:1158:1', 'test-tibblify.R:1223:1', 'test-tibblify.R:1308:1', 'test-tibblify.R:1397:1', 'test-tibblify.R:1405:1', 'test-tibblify.R:1467:1', 'test-tibblify.R:1473:1', 'test-tibblify.R:1484:1', 'test-unnest-tree.R:39:1', 'test-unnest-tree.R:190:1', 'test-unnest-tree.R:257:1', 'test-unpack-tspec.R:75:1', 'test-unpack-tspec.R:122:1', 'test-untibblify.R:185:1' • improve guessing logic (1): 'test-spec_guess_object_list.R:128:3' ══ Failed tests ════════════════════════════════════════════════════════════════ ── Error ('test-unnest-tree.R:34:3'): checks arguments ───────────────────────── Error in `UseMethod("parse_all")`: no applicable method for 'parse_all' applied to an object of class "NULL" Backtrace: ▆ 1. └─testthat::expect_snapshot(...) at test-unnest-tree.R:34:3 2. └─testthat:::expect_snapshot_(...) 3. ├─testthat:::with_is_snapshotting(...) 4. └─testthat:::verify_exec(quo_get_expr(x), quo_get_env(x), replay) 5. └─evaluate::evaluate(source, envir = env, new_device = FALSE, output_handler = handler) 6. └─evaluate::parse_all(input, filename = filename) [ FAIL 1 | WARN 0 | SKIP 85 | PASS 541 ] Error: ! Test failures. Execution halted Package: tinyProject Check: tests New result: ERROR Running ‘testthat.R’ [2s/2s] Running the tests in ‘tests/testthat.R’ failed. Complete output: > library(testthat) > library(tinyProject) > > test_check("tinyProject") Saving _problems/test-commandArgs-41.R [ FAIL 1 | WARN 5 | SKIP 0 | PASS 73 ] ══ Failed tests ════════════════════════════════════════════════════════════════ ── Error ('test-commandArgs.R:3:1'): (code run outside of `test_that()`) ─────── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test-commandArgs.R:3:1 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) [ FAIL 1 | WARN 5 | SKIP 0 | PASS 73 ] Error: ! Test failures. Execution halted Package: TrialEmulation Check: tests New result: ERROR Running ‘testthat.R’ [166s/153s] Running the tests in ‘tests/testthat.R’ failed. Complete output: > library(testthat) > library(TrialEmulation) > > data.table::setDTthreads(2) > test_check("TrialEmulation") Starting data manipulation Starting data extension Summary of extended data: Number of observations: 1939053 ------------------------------------------------------------ Saving _problems/test-data_preparation-18.R Starting data manipulation P(treatment = 1 | previous treatment = 0) for denominator Call: glm(formula = treatment ~ 1, family = binomial(link = "logit"), data = data) Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -3.84325 0.04785 -80.31 <2e-16 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 (Dispersion parameter for binomial family taken to be 1) Null deviance: 4329.7 on 21263 degrees of freedom Residual deviance: 4329.7 on 21263 degrees of freedom AIC: 4331.7 Number of Fisher Scoring iterations: 6 P(treatment = 1 | previous treatment = 0) for numerator Call: glm(formula = treatment ~ 1, family = binomial(link = "logit"), data = data) Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -3.84325 0.04785 -80.31 <2e-16 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 (Dispersion parameter for binomial family taken to be 1) Null deviance: 4329.7 on 21263 degrees of freedom Residual deviance: 4329.7 on 21263 degrees of freedom AIC: 4331.7 Number of Fisher Scoring iterations: 6 P(treatment = 1 | previous treatment = 1) for denominator Call: glm(formula = treatment ~ 1, family = binomial(link = "logit"), data = data) Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) 4.37616 0.06815 64.21 <2e-16 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 (Dispersion parameter for binomial family taken to be 1) Null deviance: 2346.7 on 17555 degrees of freedom Residual deviance: 2346.7 on 17555 degrees of freedom AIC: 2348.7 Number of Fisher Scoring iterations: 7 P(treatment = 1 | previous treatment = 1) for numerator Call: glm(formula = treatment ~ 1, family = binomial(link = "logit"), data = data) Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) 4.37616 0.06815 64.21 <2e-16 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 (Dispersion parameter for binomial family taken to be 1) Null deviance: 2346.7 on 17555 degrees of freedom Residual deviance: 2346.7 on 17555 degrees of freedom AIC: 2348.7 Number of Fisher Scoring iterations: 7 Starting data extension Summary of extended data: Number of observations: 963883 ------------------------------------------------------------ Saving _problems/test-data_preparation-126.R Starting data manipulation P(treatment = 1 | previous treatment = 0) for denominator Call: glm(formula = treatment ~ 1, family = binomial(link = "logit"), data = data) Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -3.32517 0.03599 -92.4 <2e-16 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 (Dispersion parameter for binomial family taken to be 1) Null deviance: 6948.7 on 23042 degrees of freedom Residual deviance: 6948.7 on 23042 degrees of freedom AIC: 6950.7 Number of Fisher Scoring iterations: 6 P(treatment = 1 | previous treatment = 0) for numerator Call: glm(formula = treatment ~ 1, family = binomial(link = "logit"), data = data) Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -3.32517 0.03599 -92.4 <2e-16 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 (Dispersion parameter for binomial family taken to be 1) Null deviance: 6948.7 on 23042 degrees of freedom Residual deviance: 6948.7 on 23042 degrees of freedom AIC: 6950.7 Number of Fisher Scoring iterations: 6 P(treatment = 1 | previous treatment = 1) for denominator Call: glm(formula = treatment ~ 1, family = binomial(link = "logit"), data = data) Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) 3.9124 0.0453 86.37 <2e-16 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 (Dispersion parameter for binomial family taken to be 1) Null deviance: 4892.8 on 25356 degrees of freedom Residual deviance: 4892.8 on 25356 degrees of freedom AIC: 4894.8 Number of Fisher Scoring iterations: 6 P(treatment = 1 | previous treatment = 1) for numerator Call: glm(formula = treatment ~ 1, family = binomial(link = "logit"), data = data) Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) 3.9124 0.0453 86.37 <2e-16 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 (Dispersion parameter for binomial family taken to be 1) Null deviance: 4892.8 on 25356 degrees of freedom Residual deviance: 4892.8 on 25356 degrees of freedom AIC: 4894.8 Number of Fisher Scoring iterations: 6 Starting data extension Summary of extended data: Number of observations: 1939053 ------------------------------------------------------------ Saving _problems/test-data_preparation-149.R Saving _problems/test-data_preparation-175.R Starting data manipulation P(treatment = 1 | previous treatment = 0) for denominator Call: glm(formula = treatment ~ age_s + x4 + x2 + x1, family = binomial(link = "logit"), data = data) Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -0.13699 0.25792 -0.531 0.5953 age_s -0.04014 0.19890 -0.202 0.8401 x4 1.09583 0.21355 5.131 2.88e-07 *** x2 0.09687 0.19430 0.499 0.6181 x1 0.61385 0.36325 1.690 0.0911 . --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 (Dispersion parameter for binomial family taken to be 1) Null deviance: 232.27 on 169 degrees of freedom Residual deviance: 185.41 on 165 degrees of freedom AIC: 195.41 Number of Fisher Scoring iterations: 4 P(treatment = 1 | previous treatment = 0) for numerator Call: glm(formula = treatment ~ age_s + x4, family = binomial(link = "logit"), data = data) Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) 0.12031 0.20308 0.592 0.554 age_s 0.01215 0.19512 0.062 0.950 x4 1.10863 0.21415 5.177 2.26e-07 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 (Dispersion parameter for binomial family taken to be 1) Null deviance: 232.27 on 169 degrees of freedom Residual deviance: 188.62 on 167 degrees of freedom AIC: 194.62 Number of Fisher Scoring iterations: 4 P(treatment = 1 | previous treatment = 1) for denominator Call: glm(formula = treatment ~ age_s + x4 + x2 + x1, family = binomial(link = "logit"), data = data) Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) 0.6783 0.2797 2.425 0.015318 * age_s -0.1924 0.2115 -0.910 0.362887 x4 0.8536 0.2218 3.849 0.000119 *** x2 0.3246 0.1963 1.653 0.098252 . x1 0.7923 0.4914 1.612 0.106867 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 (Dispersion parameter for binomial family taken to be 1) Null deviance: 188.83 on 150 degrees of freedom Residual deviance: 164.85 on 146 degrees of freedom AIC: 174.85 Number of Fisher Scoring iterations: 4 P(treatment = 1 | previous treatment = 1) for numerator Call: glm(formula = treatment ~ age_s + x4, family = binomial(link = "logit"), data = data) Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) 0.7476 0.2520 2.967 0.003011 ** age_s -0.1686 0.2081 -0.810 0.417888 x4 0.7935 0.2163 3.669 0.000244 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 (Dispersion parameter for binomial family taken to be 1) Null deviance: 188.83 on 150 degrees of freedom Residual deviance: 170.78 on 148 degrees of freedom AIC: 176.78 Number of Fisher Scoring iterations: 4 Starting data extension Summary of extended data: Number of observations: 500 ------------------------------------------------------------ [ FAIL 4 | WARN 0 | SKIP 33 | PASS 458 ] ══ Skipped tests (33) ══════════════════════════════════════════════════════════ • On CRAN (33): 'test-data_utils.R:1:1', 'test-data_utils.R:100:1', 'test-data_utils.R:115:1', 'test-generics.R:1:1', 'test-generics.R:41:1', 'test-generics.R:72:1', 'test-modelling.R:99:3', 'test-modelling.R:154:3', 'test-modelling.R:175:3', 'test-modelling.R:205:3', 'test-modelling.R:229:3', 'test-modelling.R:266:3', 'test-modelling.R:290:3', 'test-modelling.R:314:3', 'test-modelling.R:357:3', 'test-modelling.R:378:1', 'test-predict.R:1:1', 'test-predict.R:34:1', 'test-predict.R:86:1', 'test-predict.R:140:1', 'test-sampling.R:1:1', 'test-sampling.R:11:1', 'test-sampling.R:18:1', 'test-sampling.R:40:1', 'test-sampling.R:70:1', 'test-sampling.R:89:1', 'test-sampling.R:180:3', 'test-te_weights.R:1:1', 'test-te_weights.R:44:1', 'test-trial_sequence.R:40:1', 'test-trial_sequence.R:44:1', 'test-trial_sequence.R:48:1', 'test-trial_sequence.R:52:1' ══ Failed tests ════════════════════════════════════════════════════════════════ ── Error ('test-data_preparation.R:18:3'): data_preparation works as expected ── Error in `expect(nrow(result$data), result$N)`: `ok` must be `TRUE` or `FALSE`, not the number 1939053. Backtrace: ▆ 1. └─testthat::expect(ok = nrow(result$data)) at test-data_preparation.R:18:3 2. └─testthat:::check_bool(ok) 3. └─testthat:::stop_input_type(...) 4. └─rlang::abort(message, ..., call = call, arg = arg) ── Error ('test-data_preparation.R:126:3'): data_preparation works with PP estimand type ── Error in `expect(nrow(result$data), result$N)`: `ok` must be `TRUE` or `FALSE`, not the number 963883. Backtrace: ▆ 1. └─testthat::expect(ok = nrow(result$data)) at test-data_preparation.R:126:3 2. └─testthat:::check_bool(ok) 3. └─testthat:::stop_input_type(...) 4. └─rlang::abort(message, ..., call = call, arg = arg) ── Error ('test-data_preparation.R:149:3'): data_preparation works with As-Treated estimand type ── Error in `expect(nrow(result$data), result$N)`: `ok` must be `TRUE` or `FALSE`, not the number 1939053. Backtrace: ▆ 1. └─testthat::expect(ok = nrow(result$data)) at test-data_preparation.R:149:3 2. └─testthat:::check_bool(ok) 3. └─testthat:::stop_input_type(...) 4. └─rlang::abort(message, ..., call = call, arg = arg) ── Error ('test-data_preparation.R:175:3'): data_preparation works with ITT and censor weights ── Error in `expect(nrow(result$data), result$N)`: `ok` must be `TRUE` or `FALSE`, not the number 8795. Backtrace: ▆ 1. └─testthat::expect(ok = nrow(result$data)) at test-data_preparation.R:175:3 2. └─testthat:::check_bool(ok) 3. └─testthat:::stop_input_type(...) 4. └─rlang::abort(message, ..., call = call, arg = arg) [ FAIL 4 | WARN 0 | SKIP 33 | PASS 458 ] Error: ! Test failures. Execution halted Package: tryCatchLog Check: tests New result: ERROR Running ‘testthat.R’ [6s/6s] Running the tests in ‘tests/testthat.R’ failed. Complete output: > library(testthat) > library(tryCatchLog) Using futile.logger for logging... > > > > # Set to something like [1] "en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/de_DE.UTF-8" > # to ensure english error messages > # DISABLED - DOES NOT WORK (AT LEAST NOT ON OSX)! > # Sys.setlocale("LC_ALL", "en_US.UTF-8") > # Sys.getlocale() > > # https://stackoverflow.com/questions/47977951/how-to-ensure-english-error-messages-in-testthat-unit-tests > Sys.setenv("LANGUAGE" = "EN") # work-around to always create english R (error) messages > > > > test_check("tryCatchLog") Saving _problems/test_build_log_output-139.R Saving _problems/test_is_windows-22.R Saving _problems/test_namespace_hooks-45.R Saving _problems/test_platform_functions-42.R [ FAIL 4 | WARN 0 | SKIP 0 | PASS 436 ] ══ Failed tests ═════════════════════════════════════════════════════════════════════════════════════════════════════════════════ ── Error ('test_build_log_output.R:132:3'): platform-specific newline works ───────────────────────────────────────────────────── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test_build_log_output.R:132:3 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) ── Error ('test_is_windows.R:17:3'): conflict in Windows OS recognition throws a warning ──────────────────────────────────────── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test_is_windows.R:17:3 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) ── Error ('test_namespace_hooks.R:36:3'): internal package state is initialized ───────────────────────────────────────────────── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test_namespace_hooks.R:36:3 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) ── Error ('test_platform_functions.R:37:3'): OS-specific newlines work ────────────────────────────────────────────────────────── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test_platform_functions.R:37:3 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) [ FAIL 4 | WARN 0 | SKIP 0 | PASS 436 ] Error: ! Test failures. Execution halted Package: typetracer Check: compiled code Old result: NOTE File ‘typetracer/libs/typetracer.so’: Found non-API call to R: ‘SET_BODY’ Compiled code should not call non-API entry points in R. See ‘Writing portable packages’ in the ‘Writing R Extensions’ manual, and section ‘Moving into C API compliance’ for issues with the use of non-API entry points. New result: WARNING File ‘typetracer/libs/typetracer.so’: Found non-API call to R: ‘SET_BODY’ This entry point may be removed soon. Compiled code should not call non-API entry points in R. See ‘Writing portable packages’ in the ‘Writing R Extensions’ manual, and section ‘Moving into C API compliance’ for issues with the use of non-API entry points. Package: vein Check: tests New result: ERROR Running ‘testthat.R’ [46s/45s] Running the tests in ‘tests/testthat.R’ failed. Complete output: > library(testthat) > library(vein) > > test_check("vein") Running tests in parallel requires the 3rd edition. Weighted mean = 6 [[1]] function (V) x[i] [[2]] function (V) x[i] [[3]] function (V) x[i] [[4]] function (V) x[i] [[5]] function (V) x[i] [[6]] function (V) x[i] [[7]] function (V) x[i] [[8]] function (V) x[i] [[9]] function (V) x[i] [[10]] function (V) x[i] [[11]] function (V) x[i] [[12]] function (V) x[i] [[13]] function (V) x[i] [[14]] function (V) x[i] [[15]] function (V) x[i] [[16]] function (V) x[i] [[17]] function (V) x[i] [[18]] function (V) x[i] [[19]] function (V) x[i] [[20]] function (V) x[i] [[21]] function (V) x[i] [[22]] function (V) x[i] [[23]] function (V) x[i] [[24]] function (V) x[i] [[25]] function (V) x[i] [[26]] function (V) x[i] [[27]] function (V) x[i] [[28]] function (V) x[i] [[29]] function (V) x[i] [[30]] function (V) x[i] [[31]] function (V) x[i] [[32]] function (V) x[i] [[33]] function (V) x[i] [[34]] function (V) x[i] [[35]] function (V) x[i] [[36]] function (V) x[i] [[37]] function (V) x[i] [[38]] function (V) x[i] [[39]] function (V) x[i] [[40]] function (V) x[i] attr(,"class") [1] "EmissionFactorsList" "list" This EmissionFactorsList has 40 functionsThis EmissionFactorsList has 40 functions Min Qu.1 Median Mean Qu.3 Max sd 1 0.002 0.134 0.876 4.024 3.694 80.182 7.999 Weighted mean = 4.49 Number of lon points: 16 Number of lat points: 19 Sum of street emissions 1768.17 Sum of gridded emissions 1768.17 Weighted mean = 1.5 Please, choose one of the following pollutants: CH4 CO2 D_0_15 D_10_25 D_20_35 ETOH FC FS KML N2O NH3 PM R_0_15 R_10_25 R_20_35 S_0_15 S_10_25 S_20_35 gCO2/KWH gD/KWH CO_0km CO NOx NOx_0km NO NO_0km NO2 NO2_0km HC_0km HC NMHC_0km NMHC RCHO RCHO_0km PM25RES PM10RES scale = default scale = default Please, choose on of the following categories: PC_G PC_E PC_FG PC_FE LCV_G LCV_E LCV_FG LCV_FE LCV_D TRUCKS_SL_D TRUCKS_L_D TRUCKS_M_D TRUCKS_SH_D TRUCKS_H_D BUS_URBAN_D BUS_MICRO_D BUS_COACH_D BUS_ARTIC_D MC_150_G MC_150_500_G MC_500_G MC_150_FG MC_150_500_FG MC_500_FG MC_150_FE MC_150_500_FE MC_500_FE CICLOMOTOR G_BEFORE_GNV G_AFTER_GNV GNV_AFTER_GNV LDV HDV MC PC_ELEC LCV_ELEC TRUCKS_ELEC BUS_ELEC MC_150_ELEC MC_150_500_ELEC MC_500_ELEC note ref 'v' must be one of: Trucks PV 't' must be one of: Mini Light Medium Heavy Low Speed Small Large Taxi Bus Motorcycles Moped Mediumbus Largebus 3-Wheel 'f' must be one of: G D CNG ALL G HY D HY D 'p' must be one of: CO HC NOx PM2.5 PM10 Please, select any of: [1] "ed" "eshotfi" "eswarmc" "eshotc" "erhotfi" "erwarmc" "erhotc" function (x, x0, L, k) { L/(1 + exp(1)^(-k * (x - x0))) } adjusting amileage adjusting ef Names of the ef data.frame: [1] "Age" "Year" "Pollutant" "Proconve_LDV" "t_Euro_LDV" [6] "Euro_LDV" "Proconve_HDV" "Euro_HDV" "PC_G" "LT" Range Year: 1980 2015 Names of the Pollutants: CO HC NMHC CH4 NOx CO2 PM N2O Names of the ef data.frame: [1] "Age" "Year" "Pollutant" "Proconve_LDV" "t_Euro_LDV" [6] "Euro_LDV" "Proconve_HDV" "Euro_HDV" "PC_G" "LT" Range Year: 1980 2015 Names of the Pollutants: CO HC NMHC CH4 NOx CO2 PM N2O Please, choose one of the following pollutants: CO HC NMHC CH4 NOx CO2 PM N2O Names of the ef data.frame: [1] "Age" "Year" "Pollutant" "Proconve_LDV" "t_Euro_LDV" [6] "Euro_LDV" "Proconve_HDV" "Euro_HDV" "PC_G" "LT" Range Year: 1980 2015 Names of the ef data.frame: [1] "Age" "Year" "Pollutant" "Proconve_LDV" "t_Euro_LDV" [6] "Euro_LDV" "Proconve_HDV" "Euro_HDV" "PC_G" "LT" Range Year: 1980 2015 Names of the Pollutants: CO HC NMHC CH4 NOx CO2 PM N2O Names of the ef data.frame: [1] "Age" "Year" "Pollutant" "Proconve_LDV" "t_Euro_LDV" [6] "Euro_LDV" "Proconve_HDV" "Euro_HDV" "PC_G" "LT" Range Year: 1980 2015 Names of the Pollutants: CO HC NMHC CH4 NOx CO2 PM N2O Names of the ef data.frame: [1] "Age" "Year" "Pollutant" "Proconve_LDV" "t_Euro_LDV" [6] "Euro_LDV" "Proconve_HDV" "Euro_HDV" "PC_G" "LT" Range Year: 1980 2015 Names of the Pollutants: CO HC NMHC CH4 NOx CO2 PM N2O Sum of emissions: 2412883 Sum of emissions: 2412883 Sum of emissions: 2412883 Sum of emissions: 2412883 Sum of emissions: 1844428 Sum of emissions: 1252443 Sum of emissions: 1252443 Sum of emissions: 1844428 Sum of emissions: 1844428 Sum of emissions: 1843744 Sum of emissions: 1843744 Sum of emissions: 1843744 Sum of emissions: 1843744 Sum of emissions: 1255381 Sum of emissions: 1255381 Columns: geometry emission Selecting: motorway motorway_link trunk trunk_link primary primary_link secondary secondary_link tertiary tertiary_link Columns: emission highway geometry Sum of emissions: 13255 Sum of emissions: 13255 Sum of emissions: 1155 Sum of emissions: 1650 Number of lon points: 16 Number of lat points: 19 Sum of street emissions 12752 Sum of gridded emissions 12752 Sum of street emissions 12752 Sum of gridded emissions 12752 Sum of point emissions 12752 Sum of gridded emissions 12752 Sum of point emissions 1 Sum of gridded emissions 1 Sum of gridded emissions 1 Sum of street emissions 12752 Sum of gridded emissions 12752 Sum of point emissions 12752 Sum of gridded emissions 12752 Sum of point emissions 1 Sum of gridded emissions 1 Sum of gridded emissions 1 Sum of emissions: 6090769 Sum of emissions: 6090769 Sum of emissions: 6090769 Sum of emissions: 6090769 Sum of emissions: 6090769 Sum of emissions: 6090769 Sum of emissions: 6090769 Sum of emissions: 6090769 Sum of emissions: 6090769 Sum of emissions: 6090769 Sum of emissions: 6090769 Sum of emissions: 6090769 Sum of emissions: 6090769 Sum of emissions: 6090769 Sum of emissions: 6090769 Sum of emissions: 6090769 Sum of emissions: 6090769 Sum of emissions: 776986.6 Sum of emissions: 776986.6 Sum of emissions: 776986.6 Sum of emissions: 776986.6 Sum of emissions: 776986.6 Sum of emissions: 776986.6 Sum of emissions: 778797.7 Sum of emissions: 776986.6 Sum of emissions: 776986.6 Sum of emissions: 776986.6 Sum of emissions: 776986.6 Sum of emissions: 776986.6 Sum of emissions: 14127.03 Sum of emissions: 14127.03 Sum of emissions: 776986.6 Transforming into data.frame Your local_tz is: Europe/Vienna Difference with UTC: -23 Number of lon points: 15 Number of lat points: 19 Sum of street emissions 3762.47 Sum of gridded emissions 3762.47 Scripts: [1] "est/BUS_01_input.R" "est/HGV_01_input.R" "est/LCV_01_input.R" [4] "est/MC_01_input.R" "est/PC_01_input.R" "main.R" [7] "post.R" "traffic.R" Number of lon points: 23 Number of lat points: 19 Sum of street emissions 2095073 Sum of gridded emissions 2095073 Number of lon points: 23 Number of lat points: 19 Number of lon points: 23 Number of lat points: 19 Number of lon points: 23 Number of lat points: 19 Saving _problems/test-make_grid-27.R Number of lon points: 1 Number of lat points: 1 Speeds by columns and street in study area = Min. 1st Qu. Median Mean 3rd Qu. Max. 1 1 1 1 1 1 Number of lon points: 23 Number of lat points: 21 Total Emissions 1946.955 kg Intersecting Total Emissions 1946.955 kg Total Emissions 2095.073 kg Intersecting Total Emissions 2095.073 kg Adding Column Creating grid Number of lon points: 12 Number of lat points: 11 Total Emissions 2095.073 kg Intersecting Total Emissions 2095.073 kg [ FAIL 1 | WARN 0 | SKIP 2 | PASS 640 ] ══ Skipped tests (2) ═══════════════════════════════════════════════════════════ • empty test (2): , ══ Failed tests ════════════════════════════════════════════════════════════════ ── Error ('test-make_grid.R:26:3'): emis_grid warning ────────────────────────── Error in `expect_warning(make_grid(b, 1/102.47/2, crs = "+init=epsg:31983")$id[1], 1)`: `regexp` must be a single string, `NA`, or `NULL`, not the number 1. Backtrace: ▆ 1. └─testthat::expect_warning(regexp = 1) at test-make_grid.R:26:3 2. └─testthat:::check_string(regexp, allow_null = TRUE, allow_na = TRUE) 3. └─testthat:::stop_input_type(...) 4. └─rlang::abort(message, ..., call = call, arg = arg) [ FAIL 1 | WARN 0 | SKIP 2 | PASS 640 ] Error: ! Test failures. Execution halted Package: WhatIf Check: tests New result: ERROR Running ‘testthat.R’ [1s/1s] Running the tests in ‘tests/testthat.R’ failed. 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|===================================================================== | 98% | |===================================================================== | 99% | |======================================================================| 100% Saving _problems/test-whatif_convexhull-20.R [ FAIL 1 | WARN 0 | SKIP 4 | PASS 2 ] ══ Skipped tests (4) ═══════════════════════════════════════════════════════════ • On CRAN (4): 'test-whatif.R:9:5', 'test-whatif.R:32:5', 'test-whatif.R:55:5', 'test-whatif_convexhull.R:2:5' ══ Failed tests ════════════════════════════════════════════════════════════════ ── Error ('test-whatif_convexhull.R:16:5'): REQUIRE TEST multitreaded ────────── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test-whatif_convexhull.R:16:5 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) [ FAIL 1 | WARN 0 | SKIP 4 | PASS 2 ] Error: ! Test failures. Execution halted Package: ZillowR Check: tests New result: ERROR Running ‘spelling.R’ [0s/0s] Running ‘testthat.R’ [2s/2s] Running the tests in ‘tests/testthat.R’ failed. Complete output: > library(testthat) > library(ZillowR) > > test_check("ZillowR") Saving _problems/test-GetChart-11.R Saving _problems/test-GetComps-11.R Saving _problems/test-GetDeepComps-11.R Saving _problems/test-GetDeepSearchResults-11.R Saving _problems/test-GetMonthlyPayments-11.R Saving _problems/test-GetRateSummary-11.R Saving _problems/test-GetSearchResults-11.R Saving _problems/test-GetUpdatedPropertyDetails-11.R Saving _problems/test-GetZestimate-11.R [ FAIL 9 | WARN 0 | SKIP 0 | PASS 125 ] ══ Failed tests ════════════════════════════════════════════════════════════════ ── Error ('test-GetChart.R:7:5'): 'getURL' errors are handled gracefully ─────── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test-GetChart.R:7:5 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) ── Error ('test-GetComps.R:7:5'): 'getURL' errors are handled gracefully ─────── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test-GetComps.R:7:5 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) ── Error ('test-GetDeepComps.R:7:5'): 'getURL' errors are handled gracefully ─── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test-GetDeepComps.R:7:5 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) ── Error ('test-GetDeepSearchResults.R:7:5'): 'getURL' errors are handled gracefully ── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test-GetDeepSearchResults.R:7:5 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) ── Error ('test-GetMonthlyPayments.R:7:5'): 'getURL' errors are handled gracefully ── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test-GetMonthlyPayments.R:7:5 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) ── Error ('test-GetRateSummary.R:7:5'): 'getURL' errors are handled gracefully ── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test-GetRateSummary.R:7:5 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) ── Error ('test-GetSearchResults.R:7:5'): 'getURL' errors are handled gracefully ── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test-GetSearchResults.R:7:5 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) ── Error ('test-GetUpdatedPropertyDetails.R:7:5'): 'getURL' errors are handled gracefully ── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test-GetUpdatedPropertyDetails.R:7:5 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) ── Error ('test-GetZestimate.R:7:5'): 'getURL' errors are handled gracefully ─── Error: `with_mock()` was deprecated in testthat 3.2.0 and is now defunct. ℹ Please use `with_mocked_bindings()` instead. Backtrace: ▆ 1. └─testthat::with_mock(...) at test-GetZestimate.R:7:5 2. └─lifecycle::deprecate_stop("3.2.0", "with_mock()", "with_mocked_bindings()") 3. └─lifecycle:::deprecate_stop0(msg) 4. └─rlang::cnd_signal(...) [ FAIL 9 | WARN 0 | SKIP 0 | PASS 125 ] Error: ! Test failures. Execution halted