R Under development (unstable) (2024-05-26 r86629 ucrt) -- "Unsuffered Consequences" Copyright (C) 2024 The R Foundation for Statistical Computing Platform: x86_64-w64-mingw32/x64 R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > # 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(bmm) > > test_check("bmm") No formula for parameter kappa provided. Only a fixed intercept will be estimated. Your data has been sorted by the following predictors: * caution: you have set `sort_data=TRUE`. You need to be careful when using brms postprocessing methods that rely on the data order, such as generating predictions. Assuming you assigned the result of bmm() to a variable called `fit`, you can extract the sorted data from the fitted object with: data_sorted <- fit$data Your data has been sorted by the following predictors: * caution: you have set `sort_data=TRUE`. You need to be careful when using brms postprocessing methods that rely on the data order, such as generating predictions. Assuming you assigned the result of bmm() to a variable called `fit`, you can extract the sorted data from the fitted object with: data_sorted <- fit$data Your data has been sorted by the following predictors: * caution: you have set `sort_data=TRUE`. You need to be careful when using brms postprocessing methods that rely on the data order, such as generating predictions. Assuming you assigned the result of bmm() to a variable called `fit`, you can extract the sorted data from the fitted object with: data_sorted <- fit$data [ FAIL 0 | WARN 0 | SKIP 14 | PASS 305 ] ══ Skipped tests (14) ══════════════════════════════════════════════════════════ • On CRAN (10): 'test-bmm.R:3:3', 'test-bmm.R:58:3', 'test-helpers-model.R:83:3', 'test-model_imm.R:4:3', 'test-model_imm.R:17:3', 'test-model_imm.R:31:3', 'test-model_imm.R:46:3', 'test-model_imm.R:100:3', 'test-model_mixture3p.R:2:3', 'test-model_mixture3p.R:19:3' • Skipping (1): 'test-restructure.R:10:3' • empty test (1): 'test-helpers-model.R:126:1' • interactive() is not TRUE (2): 'test-summary.R:2:3', 'test-update.R:4:3' [ FAIL 0 | WARN 0 | SKIP 14 | PASS 305 ] > > proc.time() user system elapsed 25.70 1.07 26.73