Package: actuaRE
Check: for code/documentation mismatches
New result: WARNING
  Codoc mismatches from Rd file 'findbars.Rd':
  findbars
    Code: function(...)
    Docs: function(term)
    Argument names in code not in docs:
      ...
    Argument names in docs not in code:
      term
    Mismatches in argument names:
      Position: 1 Code: ... Docs: term
  
  Codoc mismatches from Rd file 'isNested.Rd':
  isNested
    Code: function(...)
    Docs: function(f1, f2)
    Argument names in code not in docs:
      ...
    Argument names in docs not in code:
      f1 f2
    Mismatches in argument names:
      Position: 1 Code: ... Docs: f1
  
  Codoc mismatches from Rd file 'nobars.Rd':
  nobars
    Code: function(...)
    Docs: function(term)
    Argument names in code not in docs:
      ...
    Argument names in docs not in code:
      term
    Mismatches in argument names:
      Position: 1 Code: ... Docs: term

Package: ggeffects
Check: tests
New result: ERROR
    Running ‘testthat.R’ [97s/97s]
  Running the tests in ‘tests/testthat.R’ failed.
  Complete output:
    > library(testthat)
    > library(ggeffects)
    > test_check("ggeffects")
    You are calculating adjusted predictions on the population-level (i.e.
      `type = "fixed"`) for a *generalized* linear mixed model.
      This may produce biased estimates due to Jensen's inequality. Consider
      setting `bias_correction = TRUE` to correct for this bias.
      See also the documentation of the `bias_correction` argument.
    
    Not all rows are shown in the output. Use `print(..., n = Inf)` to show
      all rows.
    
    Not all rows are shown in the output. Use `print(..., n = Inf)` to show
      all rows.
    Running model...
    Done!
    Some of the focal terms are of type `character`. This may lead to
      unexpected results. It is recommended to convert these variables to
      factors before fitting the model.
      The following variables are of type character: `brand`
    Some of the focal terms are of type `character`. This may lead to
      unexpected results. It is recommended to convert these variables to
      factors before fitting the model.
      The following variables are of type character: `brand`
    Some of the focal terms are of type `character`. This may lead to
      unexpected results. It is recommended to convert these variables to
      factors before fitting the model.
      The following variables are of type character: `brand`
    
    Iteration 1 - deviance = 39.74973 - criterion = 0.8590917
    Iteration 2 - deviance = 10.50328 - criterion = 2.758244
    Iteration 3 - deviance = 9.231325 - criterion = 0.1363107
    Iteration 4 - deviance = 9.227742 - criterion = 0.0003840654
    Iteration 5 - deviance = 9.227742 - criterion = 3.446463e-09
    converged
    NOTE: Results may be misleading due to involvement in interactions
    NOTE: Results may be misleading due to involvement in interactions
    
    Re-fitting to get Hessian
    
    
    Re-fitting to get Hessian
    
    
    Re-fitting to get Hessian
    
    
    Re-fitting to get Hessian
    
    
    Re-fitting to get Hessian
    
    Could not compute variance-covariance matrix of predictions. No
      confidence intervals are returned.
    Saving _problems/test-poly-zeroinf-27.R
    Can't compute adjusted predictions, `effects::Effect()` returned an error.
    
    Reason: Invalid operation on a survival time
    You may try `ggpredict()` or `ggemmeans()`.
    
    Can't compute adjusted predictions, `effects::Effect()` returned an error.
    
    Reason: non-conformable arguments
    You may try `ggpredict()` or `ggemmeans()`.
    
    [ FAIL 1 | WARN 1 | SKIP 70 | PASS 496 ]
    
    ══ Skipped tests (70) ══════════════════════════════════════════════════════════
    • On CRAN (58): 'test-MCMCglmm.R:1:1', 'test-MixMod.R:1:1',
      'test-averaging.R:1:1', 'test-avg_predictions.R:1:1',
      'test-backtransform_response.R:1:1', 'test-bias_correction.R:1:1',
      'test-brms-categ-cum.R:1:1', 'test-brms-monotonic.R:1:1',
      'test-brms-ppd.R:1:1', 'test-brms-trial.R:1:1', 'test-clean_vars.R:1:1',
      'test-clm.R:1:1', 'test-clm2.R:1:1', 'test-clmm.R:1:1',
      'test-condition.R:1:1', 'test-correct_se_sorting.R:1:1',
      'test-decimals.R:1:1', 'test-fixest.R:1:1', 'test-focal_only_random.R:1:1',
      'test-format.R:1:1', 'test-gamlss.R:1:1', 'test-gamm4.R:1:1',
      'test-glmer.R:2:1', 'test-glmmTMB.R:1:1', 'test-interval_re.R:1:1',
      'test-ivreg.R:1:1', 'test-list_terms.R:32:1', 'test-lmer.R:1:1',
      'test-mgcv.R:1:1', 'test-plot-from-vignettes.R:5:1',
      'test-plot-ordinal-latent.R:1:1', 'test-plot-show_data.R:1:1',
      'test-plot-survival.R:2:1', 'test-plot.R:1:1', 'test-polr.R:13:5',
      'test-polr.R:43:5', 'test-pool_comparisons.R:1:1', 'test-print.R:1:1',
      'test-print_digits.R:1:1', 'test-print_md.R:1:1',
      'test-print_zero_inflation.R:1:1', 'test-resid_over_grid.R:33:5',
      'test-rstanarm-ppd.R:1:1', 'test-rstanarm.R:1:1', 'test-sdmTMB.R:1:1',
      'test-simulate.R:1:1', 'test-test_predictions-margin.R:1:1',
      'test-test_predictions-mixed.R:1:1', 'test-test_predictions_emmeans.R:58:1',
      'test-test_predictions_emmeans.R:73:1',
      'test-test_predictions_ggeffects.R:115:1',
      'test-test_predictions_ggeffects.R:164:1',
      'test-test_predictions_ggeffects.R:183:3',
      'test-test_predictions_ggeffects.R:226:5', 'test-vcov.R:1:1',
      'test-vglm.R:1:1', 'test-zeroinfl.R:27:3', 'test-zi_prob.R:1:1'
    • On Linux (10): 'test-brglm.R:1:1', 'test-ci_backticks-names.R:1:1',
      'test-emmeans-weights.R:1:1', 'test-gee.R:1:1', 'test-ggaverage.R:1:1',
      'test-glm.R:1:1', 'test-ordinal.R:1:1', 'test-print_subsets.R:1:1',
      'test-print_test_predictions-ordinal.R:1:1',
      'test-print_test_predictions.R:1:1'
    • empty test (2): 'test-polr.R:136:5', 'test-polr.R:142:5'
    
    ══ Failed tests ════════════════════════════════════════════════════════════════
    ── Error ('test-poly-zeroinf.R:27:3'): ggpredict, glmmTMB ──────────────────────
    Error in `exp(prdat.sim$cond)`: non-numeric argument to mathematical function
    Backtrace:
        ▆
     1. └─ggeffects::ggpredict(...) at test-poly-zeroinf.R:27:3
     2.   ├─base::do.call(ggpredict_helper, full_args)
     3.   └─ggeffects (local) `<fn>`(...)
     4.     ├─ggeffects::get_predictions(...)
     5.     └─ggeffects:::get_predictions.glmmTMB(...)
    
    [ FAIL 1 | WARN 1 | SKIP 70 | PASS 496 ]
    Error:
    ! Test failures.
    Execution halted

Package: insight
Check: examples
New result: ERROR
  Running examples in ‘insight-Ex.R’ failed
  The error most likely occurred in:
  
  > base::assign(".ptime", proc.time(), pos = "CheckExEnv")
  > ### Name: is_converged
  > ### Title: Convergence test for mixed effects models
  > ### Aliases: is_converged
  > 
  > ### ** Examples
  > 
  > ## Don't show: 
  > if (require("lme4", quietly = TRUE)) withAutoprint({ # examplesIf
  + ## End(Don't show)
  + library(lme4)
  + data(cbpp)
  + set.seed(1)
  + cbpp$x <- rnorm(nrow(cbpp))
  + cbpp$x2 <- runif(nrow(cbpp))
  + 
  + model <- glmer(
  +   cbind(incidence, size - incidence) ~ period + x + x2 + (1 + x | herd),
  +   data = cbpp,
  +   family = binomial()
  + )
  + 
  + is_converged(model)
  + ## Don't show: 
  + }) # examplesIf
  > library(lme4)
  > data(cbpp)
  > set.seed(1)
  > cbpp$x <- rnorm(nrow(cbpp))
  > cbpp$x2 <- runif(nrow(cbpp))
  > model <- glmer(cbind(incidence, size - incidence) ~ period + x + x2 + 
  +     (1 + x | herd), data = cbpp, family = binomial())
  boundary (singular) fit: see help('isSingular')
  > is_converged(model)
  Error in h(simpleError(msg, call)) : 
    error in evaluating the argument 'a' in selecting a method for function 'solve': object 'Hessian' not found
  Calls: withAutoprint ... eval -> eval -> <Anonymous> -> .handleSimpleError -> h
  Execution halted

Package: insight
Check: tests
New result: ERROR
    Running ‘testthat.R’ [291s/142s]
  Running the tests in ‘tests/testthat.R’ failed.
  Complete output:
    > library(testthat)
    > library(insight)
    > test_check("insight")
    Starting 2 test processes.
    > test-find_transformation.R: boundary (singular) fit: see help('isSingular')
    > test-gamlss.R: GAMLSS-RS iteration 1: Global Deviance = 365.2328 
    > test-gamlss.R: GAMLSS-RS iteration 2: Global Deviance = 365.1292 
    > test-gamlss.R: GAMLSS-RS iteration 3: Global Deviance = 365.1269 
    > test-gamlss.R: GAMLSS-RS iteration 4: Global Deviance = 365.1268 
    > test-gamlss.R: GAMLSS-RS iteration 1: Global Deviance = 5779.746 
    > test-gamlss.R: GAMLSS-RS iteration 2: Global Deviance = 5779.746 
    > test-gamlss.R: GAMLSS-RS iteration 1: Global Deviance = 703.1164 
    > test-gamlss.R: GAMLSS-RS iteration 2: Global Deviance = 703.1164 
    > test-get_model.R: Loading required namespace: GPArotation
    > test-get_random.R: boundary (singular) fit: see help('isSingular')
    > test-glmmPQL.R: iteration 1
    Saving _problems/test-is_converged-16.R
    > test-mmrm.R: mmrm() registered as emmeans extension
    > test-mmrm.R: mmrm() registered as car::Anova extension
    > test-model_info.R: boundary (singular) fit: see help('isSingular')
    > test-nestedLogit.R: list(work = c(0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 
    > test-nestedLogit.R: 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
    > test-nestedLogit.R: 0L, 1L, 1L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 
    > test-nestedLogit.R: 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 
    > test-nestedLogit.R: 1L, 1L, 0L, 0L, 0L, 1L, 1L, 1L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 1L, 
    > test-nestedLogit.R: 1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 
    > test-nestedLogit.R: 1L, 1L, 0L, 0L, 1L, 0L, 0L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 0L, 0L
    > test-nestedLogit.R: ), full = c(1L, 1L, 0L, 1L, 1L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 
    > test-nestedLogit.R: 1L, 0L, 1L, 1L, 0L, 1L, 0L, 0L, 0L, 1L, 1L, 0L, 1L, 1L, 0L, 0L, 
    > test-nestedLogit.R: 0L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L))
    > test-polr.R: 
    > test-polr.R: Re-fitting to get Hessian
    > test-polr.R: 
    > test-polr.R: 
    > test-polr.R: Re-fitting to get Hessian
    > test-polr.R: 
    > test-survey_coxph.R: Stratified Independent Sampling design (with replacement)
    > test-survey_coxph.R: dpbc <- survey::svydesign(
    > test-survey_coxph.R:   id = ~1,
    > test-survey_coxph.R:   prob = ~randprob,
    > test-survey_coxph.R:   strata = ~edema,
    > test-survey_coxph.R:   data = subset(pbc, randomized)
    > test-survey_coxph.R: )
    > test-survey_coxph.R: Stratified Independent Sampling design (with replacement)
    > test-survey_coxph.R: dpbc <- survey::svydesign(
    > test-survey_coxph.R:   id = ~1,
    > test-survey_coxph.R:   prob = ~randprob,
    > test-survey_coxph.R:   strata = ~edema,
    > test-survey_coxph.R:   data = subset(pbc, randomized)
    > test-survey_coxph.R: )
    > test-survey_coxph.R: Stratified Independent Sampling design (with replacement)
    > test-survey_coxph.R: dpbc <- survey::svydesign(
    > test-survey_coxph.R:   id = ~1,
    > test-survey_coxph.R:   prob = ~randprob,
    > test-survey_coxph.R:   strata = ~edema,
    > test-survey_coxph.R:   data = subset(pbc, randomized)
    > test-survey_coxph.R: )
    [ FAIL 1 | WARN 6 | SKIP 96 | PASS 3512 ]
    
    ══ Skipped tests (96) ══════════════════════════════════════════════════════════
    • On CRAN (89): 'test-GLMMadaptive.R:2:1', 'test-averaging.R:1:1',
      'test-bias_correction.R:1:1', 'test-blmer.R:262:3', 'test-betareg.R:197:5',
      'test-brms.R:1:1', 'test-brms_aterms.R:1:1',
      'test-brms_gr_random_effects.R:1:1', 'test-brms_mm.R:1:1',
      'test-brms_missing.R:1:1', 'test-brms_von_mises.R:1:1',
      'test-clean_names.R:109:3', 'test-clean_parameters.R:1:1',
      'test-coxme.R:1:1', 'test-clmm.R:170:3', 'test-cpglmm.R:152:3',
      'test-display.R:1:1', 'test-display.R:15:1', 'test-export_table.R:3:1',
      'test-export_table.R:7:1', 'test-export_table.R:134:3',
      'test-export_table.R:164:3', 'test-export_table.R:193:1',
      'test-export_table.R:278:1', 'test-export_table.R:296:3',
      'test-export_table.R:328:3', 'test-export_table.R:385:1',
      'test-export_table.R:406:3', 'test-export_table.R:470:3',
      'test-find_smooth.R:39:3', 'test-fixest.R:2:1', 'test-find_random.R:43:3',
      'test-format_table.R:2:1', 'test-format_table_ci.R:72:1', 'test-gam.R:2:1',
      'test-get_data.R:507:1', 'test-get_loglikelihood.R:143:3',
      'test-get_loglikelihood.R:223:3', 'test-get_predicted.R:2:1',
      'test-get_priors.R:1:1', 'test-get_varcov.R:43:3', 'test-get_varcov.R:57:3',
      'test-get_datagrid.R:1068:3', 'test-get_datagrid.R:1105:5',
      'test-is_converged.R:32:1', 'test-iv_robust.R:120:3', 'test-lavaan.R:1:1',
      'test-lcmm.R:1:1', 'test-lme.R:28:3', 'test-lme.R:212:3',
      'test-glmmTMB.R:67:3', 'test-glmmTMB.R:767:3', 'test-glmmTMB.R:803:3',
      'test-glmmTMB.R:1142:3', 'test-marginaleffects.R:1:1', 'test-mgcv.R:1:1',
      'test-mipo.R:1:1', 'test-mlogit.R:1:1', 'test-model_info.R:106:3',
      'test-modelbased.R:1:1', 'test-mvrstanarm.R:1:1', 'test-null_model.R:85:3',
      'test-panelr-asym.R:165:3', 'test-panelr.R:295:3', 'test-phylolm.R:1:1',
      'test-print_parameters.R:1:1', 'test-r2_nakagawa_bernoulli.R:1:1',
      'test-r2_nakagawa_beta.R:1:1', 'test-r2_nakagawa_binomial.R:1:1',
      'test-r2_nakagawa_gamma.R:1:1', 'test-r2_nakagawa_linear.R:1:1',
      'test-r2_nakagawa_negbin.R:1:1', 'test-r2_nakagawa_negbin_zi.R:1:1',
      'test-r2_nakagawa_ordered_beta.R:1:1', 'test-r2_nakagawa_poisson.R:1:1',
      'test-r2_nakagawa_poisson_zi.R:1:1',
      'test-r2_nakagawa_truncated_poisson.R:1:1', 'test-r2_nakagawa_tweedie.R:1:1',
      'test-rlmer.R:276:3', 'test-rms.R:1:1', 'test-rqss.R:1:1',
      'test-rstanarm.R:1:1', 'test-sdmTMB.R:1:1', 'test-selection.R:2:1',
      'test-spatial.R:2:1', 'test-svylme.R:1:1', 'test-tidymodels.R:1:1',
      'test-vgam.R:2:1', 'test-weightit.R:1:1'
    • On Linux (3): 'test-BayesFactorBF.R:1:1', 'test-MCMCglmm.R:1:1',
      'test-get_data.R:161:3'
    • Package `logistf` is loaded and breaks `mmrm::mmrm()` (1): 'test-mmrm.R:4:1'
    • works interactively (2): 'test-coxph-panel.R:34:3', 'test-coxph.R:38:3'
    • {bigglm} is not installed (1): 'test-model_info.R:24:3'
    
    ══ Failed tests ════════════════════════════════════════════════════════════════
    ── Error ('test-is_converged.R:16:3'): is_converged ────────────────────────────
    Error in `h(simpleError(msg, call))`: error in evaluating the argument 'a' in selecting a method for function 'solve': object 'Hessian' not found
    Backtrace:
         ▆
      1. ├─testthat::expect_true(is_converged(model)) at test-is_converged.R:16:3
      2. │ └─testthat::quasi_label(enquo(object), label)
      3. │   └─rlang::eval_bare(expr, quo_get_env(quo))
      4. ├─insight::is_converged(model)
      5. ├─insight:::is_converged.merMod(model)
      6. │ ├─base::with(x@optinfo$derivs, Matrix::solve(Hessian, gradient))
      7. │ ├─base::with.default(x@optinfo$derivs, Matrix::solve(Hessian, gradient))
      8. │ │ └─base::eval(substitute(expr), data, enclos = parent.frame())
      9. │ │   └─base::eval(substitute(expr), data, enclos = parent.frame())
     10. │ └─Matrix::solve(Hessian, gradient)
     11. └─base::.handleSimpleError(...)
     12.   └─base (local) h(simpleError(msg, call))
    
    [ FAIL 1 | WARN 6 | SKIP 96 | PASS 3512 ]
    Error:
    ! Test failures.
    Execution halted

Package: mice
Check: tests
New result: ERROR
    Running ‘testthat.R’ [34s/45s]
  Running the tests in ‘tests/testthat.R’ failed.
  Complete output:
    > library(testthat)
    > library(mice)
    
    Attaching package: 'mice'
    
    The following object is masked from 'package:stats':
    
        filter
    
    The following objects are masked from 'package:base':
    
        cbind, rbind
    
    > 
    > test_check("mice")
    Saving _problems/test-mice.impute.2l.bin-32.R
    [ FAIL 1 | WARN 0 | SKIP 2 | PASS 379 ]
    
    ══ Skipped tests (2) ═══════════════════════════════════════════════════════════
    • empty test (2): ,
    
    ══ Failed tests ════════════════════════════════════════════════════════════════
    ── Error ('test-mice.impute.2l.bin.R:30:1'): (code run outside of `test_that()`) ──
    Error: object 'ID' not found
    Backtrace:
        ▆
     1. ├─... %>% dplyr::select(-month) at test-mice.impute.2l.bin.R:30:1
     2. ├─dplyr::select(., -month)
     3. ├─tidyr::fill(., treatment)
     4. ├─tidyr::complete(toenail, ID, visit)
     5. └─tidyr:::complete.data.frame(toenail, ID, visit)
     6.   ├─tidyr::expand(data, ...)
     7.   └─tidyr:::expand.data.frame(data, ...)
     8.     └─tidyr:::grid_dots(..., `_data` = data)
     9.       └─rlang::eval_tidy(dot, data = mask)
    
    [ FAIL 1 | WARN 0 | SKIP 2 | PASS 379 ]
    Error:
    ! Test failures.
    Execution halted

Package: panelr
Check: tests
New result: ERROR
    Running ‘testthat.R’ [61s/61s]
  Running the tests in ‘tests/testthat.R’ failed.
  Complete output:
    > library(testthat)
    > library(panelr)
    Loading required package: lme4
    Loading required package: Matrix
    
    Attaching package: 'panelr'
    
    The following object is masked from 'package:stats':
    
        filter
    
    > 
    > test_check("panelr")
    Saving _problems/test-utils-298.R
    [ FAIL 1 | WARN 1 | SKIP 0 | PASS 292 ]
    
    ══ Failed tests ════════════════════════════════════════════════════════════════
    ── Failure ('test-utils.R:298:3'): extractors work ─────────────────────────────
    Expected `predict(mod)` to run silently.
    Actual noise: warnings.
    
    [ FAIL 1 | WARN 1 | SKIP 0 | PASS 292 ]
    Error:
    ! Test failures.
    Execution halted

Package: performance
Check: examples
New result: ERROR
  Running examples in ‘performance-Ex.R’ failed
  The error most likely occurred in:
  
  > base::assign(".ptime", proc.time(), pos = "CheckExEnv")
  > ### Name: check_convergence
  > ### Title: Convergence test for mixed effects models
  > ### Aliases: check_convergence
  > 
  > ### ** Examples
  > 
  > ## Don't show: 
  > if (require("lme4") && require("glmmTMB")) (if (getRversion() >= "3.4") withAutoprint else force)({ # examplesIf
  + ## End(Don't show)
  + data(cbpp, package = "lme4")
  + set.seed(1)
  + cbpp$x <- rnorm(nrow(cbpp))
  + cbpp$x2 <- runif(nrow(cbpp))
  + 
  + model <- lme4::glmer(
  +   cbind(incidence, size - incidence) ~ period + x + x2 + (1 + x | herd),
  +   data = cbpp,
  +   family = binomial()
  + )
  + 
  + check_convergence(model)
  + 
  + ## Don't show: 
  + }) # examplesIf
  Loading required package: lme4
  Loading required package: Matrix
  Loading required package: glmmTMB
  Warning in check_dep_version(dep_pkg = "TMB") :
    package version mismatch: 
  glmmTMB was built with TMB package version 1.9.17
  Current TMB package version is 1.9.18
  Please re-install glmmTMB from source or restore original ‘TMB’ package (see '?reinstalling' for more information)
  > data(cbpp, package = "lme4")
  > set.seed(1)
  > cbpp$x <- rnorm(nrow(cbpp))
  > cbpp$x2 <- runif(nrow(cbpp))
  > model <- lme4::glmer(cbind(incidence, size - incidence) ~ period + x + 
  +     x2 + (1 + x | herd), data = cbpp, family = binomial())
  boundary (singular) fit: see help('isSingular')
  > check_convergence(model)
  Error in h(simpleError(msg, call)) : 
    error in evaluating the argument 'a' in selecting a method for function 'solve': object 'Hessian' not found
  Calls: <Anonymous> ... eval -> eval -> <Anonymous> -> .handleSimpleError -> h
  Execution halted

Package: performance
Check: tests
New result: ERROR
    Running ‘testthat.R’ [59s/30s]
  Running the tests in ‘tests/testthat.R’ failed.
  Complete output:
    > library(testthat)
    > library(performance)
    > 
    > test_check("performance")
    Starting 2 test processes.
    > test-check_convergence.R: boundary (singular) fit: see help('isSingular')
    > test-check_collinearity.R: NOTE: 2 fixed-effect singletons were removed (2 observations).
    Saving _problems/test-check_convergence-14.R
    > test-check_itemscale.R: Some of the values are negative. Maybe affected items need to be
    > test-check_itemscale.R:   reverse-coded, e.g. using `datawizard::reverse()`.
    > test-check_itemscale.R: Some of the values are negative. Maybe affected items need to be
    > test-check_itemscale.R:   reverse-coded, e.g. using `datawizard::reverse()`.
    > test-check_itemscale.R: Some of the values are negative. Maybe affected items need to be
    > test-check_itemscale.R:   reverse-coded, e.g. using `datawizard::reverse()`.
    > test-check_itemscale.R: Some of the values are negative. Maybe affected items need to be
    > test-check_itemscale.R:   reverse-coded, e.g. using `datawizard::reverse()`.
    > test-check_itemscale.R: Some of the values are negative. Maybe affected items need to be
    > test-check_itemscale.R:   reverse-coded, e.g. using `datawizard::reverse()`.
    > test-check_itemscale.R: Some of the values are negative. Maybe affected items need to be
    > test-check_itemscale.R:   reverse-coded, e.g. using `datawizard::reverse()`.
    > test-check_itemscale.R: Some of the values are negative. Maybe affected items need to be
    > test-check_itemscale.R:   reverse-coded, e.g. using `datawizard::reverse()`.
    > test-check_itemscale.R: Some of the values are negative. Maybe affected items need to be
    > test-check_itemscale.R:   reverse-coded, e.g. using `datawizard::reverse()`.
    > test-check_itemscale.R: Some of the values are negative. Maybe affected items need to be
    > test-check_itemscale.R:   reverse-coded, e.g. using `datawizard::reverse()`.
    > test-check_itemscale.R: Some of the values are negative. Maybe affected items need to be
    > test-check_itemscale.R:   reverse-coded, e.g. using `datawizard::reverse()`.
    > test-check_overdispersion.R: Overdispersion detected.
    > test-check_overdispersion.R: Underdispersion detected.
    > test-glmmPQL.R: iteration 1
    > test-item_discrimination.R: Some of the values are negative. Maybe affected items need to be
    > test-item_discrimination.R:   reverse-coded, e.g. using `datawizard::reverse()`.
    > test-item_discrimination.R: Some of the values are negative. Maybe affected items need to be
    > test-item_discrimination.R:   reverse-coded, e.g. using `datawizard::reverse()`.
    > test-item_discrimination.R: Some of the values are negative. Maybe affected items need to be
    > test-item_discrimination.R:   reverse-coded, e.g. using `datawizard::reverse()`.
    > test-check_outliers.R: No outliers were detected (p = 0.238).
    > test-performance_aic.R: Model was not fitted with REML, however, `estimator = "REML"`. Set
    > test-performance_aic.R:   `estimator = "ML"` to obtain identical results as from `AIC()`.
    [ FAIL 1 | WARN 2 | SKIP 41 | PASS 422 ]
    
    ══ Skipped tests (41) ══════════════════════════════════════════════════════════
    • On CRAN (37): 'test-bootstrapped_icc_ci.R:2:3',
      'test-bootstrapped_icc_ci.R:44:3', 'test-binned_residuals.R:137:3',
      'test-binned_residuals.R:164:3', 'test-check_dag.R:1:1',
      'test-check_distribution.R:1:1', 'test-check_collinearity.R:169:1',
      'test-check_collinearity.R:199:1', 'test-check_model.R:1:1',
      'test-check_itemscale.R:1:1', 'test-check_itemscale.R:95:1',
      'test-check_predictions.R:2:1', 'test-check_residuals.R:2:3',
      'test-check_singularity.R:2:3', 'test-check_singularity.R:30:3',
      'test-check_zeroinflation.R:73:3', 'test-check_zeroinflation.R:112:3',
      'test-compare_performance.R:1:1', 'test-helpers.R:1:1', 'test-icc.R:2:1',
      'test-item_omega.R:1:1', 'test-item_omega.R:31:3',
      'test-check_outliers.R:110:3', 'test-check_outliers.R:297:3',
      'test-mclogit.R:52:1', 'test-model_performance.bayesian.R:1:1',
      'test-model_performance.lavaan.R:1:1', 'test-model_performance.merMod.R:2:3',
      'test-model_performance.merMod.R:25:3', 'test-model_performance.psych.R:1:1',
      'test-model_performance.rma.R:21:1', 'test-performance_reliability.R:23:3',
      'test-pkg-ivreg.R:1:1', 'test-r2_nagelkerke.R:22:3',
      'test-r2_nakagawa.R:20:1', 'test-rmse.R:35:3',
      'test-test_likelihoodratio.R:55:1'
    • On Linux (3): 'test-nestedLogit.R:1:1', 'test-r2_bayes.R:1:1',
      'test-test_wald.R:1:1'
    • getRversion() > "4.4.0" is TRUE (1): 'test-check_outliers.R:258:3'
    
    ══ Failed tests ════════════════════════════════════════════════════════════════
    ── Error ('test-check_convergence.R:14:3'): check_convergence ──────────────────
    Error in `h(simpleError(msg, call))`: error in evaluating the argument 'a' in selecting a method for function 'solve': object 'Hessian' not found
    Backtrace:
         ▆
      1. ├─testthat::expect_true(check_convergence(model)) at test-check_convergence.R:14:3
      2. │ └─testthat::quasi_label(enquo(object), label)
      3. │   └─rlang::eval_bare(expr, quo_get_env(quo))
      4. ├─performance::check_convergence(model)
      5. │ ├─insight::is_converged(x, tolerance = tolerance, ...)
      6. │ └─insight:::is_converged.merMod(x, tolerance = tolerance, ...)
      7. │   ├─base::with(x@optinfo$derivs, Matrix::solve(Hessian, gradient))
      8. │   ├─base::with.default(x@optinfo$derivs, Matrix::solve(Hessian, gradient))
      9. │   │ └─base::eval(substitute(expr), data, enclos = parent.frame())
     10. │   │   └─base::eval(substitute(expr), data, enclos = parent.frame())
     11. │   └─Matrix::solve(Hessian, gradient)
     12. └─base::.handleSimpleError(...)
     13.   └─base (local) h(simpleError(msg, call))
    
    [ FAIL 1 | WARN 2 | SKIP 41 | PASS 422 ]
    Error:
    ! Test failures.
    Execution halted

Package: steppedwedge
Check: examples
New result: ERROR
  Running examples in ‘steppedwedge-Ex.R’ failed
  The error most likely occurred in:
  
  > base::assign(".ptime", proc.time(), pos = "CheckExEnv")
  > ### Name: print.sw_analysis
  > ### Title: Summarize a list returned by steppedwedge::analysis()
  > ### Aliases: print.sw_analysis
  > 
  > ### ** Examples
  > 
  > # Load data
  > test_data <- load_data(time ="period", cluster_id = "cluster", individual_id = NULL,
  + treatment = "trt", outcome = "outcome_bin", data = sw_data_example)
  Stepped wedge dataset loaded. Discrete time design with 15 clusters, 5 sequences, and 6 time points.
  > 
  > # Analysis example: TATE estimand for exposure times 1 through 4
  > results_tate <- analyze(dat = test_data, method = "mixed", estimand_type = "TATE",
  + estimand_time = c(1, 4), exp_time = "ETI", family = poisson)
  boundary (singular) fit: see help('isSingular')
  Error in h(simpleError(msg, call)) : 
    error in evaluating the argument 'a' in selecting a method for function 'solve': object 'Hessian' not found
  Calls: analyze ... eval -> eval -> <Anonymous> -> .handleSimpleError -> h
  Execution halted

Package: steppedwedge
Check: re-building of vignette outputs
New result: ERROR
  Error(s) in re-building vignettes:
    ...
  --- re-building ‘steppedwedge.Rmd’ using rmarkdown
  
  Quitting from steppedwedge.Rmd:109-119 [unnamed-chunk-8]
  ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
  <error/rlang_error>
  Error in `h()`:
  ! error in evaluating the argument 'a' in selecting a method for function 'solve': object 'Hessian' not found
  ---
  Backtrace:
       ▆
    1. ├─steppedwedge::analyze(...)
    2. │ └─performance::check_convergence(model_eti_mixed)
    3. │   ├─insight::is_converged(x, tolerance = tolerance, ...)
    4. │   └─insight:::is_converged.merMod(x, tolerance = tolerance, ...)
    5. │     ├─base::with(x@optinfo$derivs, Matrix::solve(Hessian, gradient))
    6. │     ├─base::with.default(x@optinfo$derivs, Matrix::solve(Hessian, gradient))
    7. │     │ └─base::eval(substitute(expr), data, enclos = parent.frame())
    8. │     │   └─base::eval(substitute(expr), data, enclos = parent.frame())
    9. │     └─Matrix::solve(Hessian, gradient)
   10. └─base::.handleSimpleError(...)
   11.   └─base (local) h(simpleError(msg, call))
  ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
  
  Error: processing vignette 'steppedwedge.Rmd' failed with diagnostics:
  error in evaluating the argument 'a' in selecting a method for function 'solve': object 'Hessian' not found
  --- failed re-building ‘steppedwedge.Rmd’
  
  SUMMARY: processing the following file failed:
    ‘steppedwedge.Rmd’
  
  Error: Vignette re-building failed.
  Execution halted

Package: steppedwedge
Check: tests
New result: ERROR
    Running ‘testthat.R’ [5s/5s]
  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(steppedwedge)
    > 
    > test_check("steppedwedge")
    Stepped wedge dataset loaded. Discrete time design with 2 clusters, 2 sequences, and 3 time points.
    boundary (singular) fit: see help('isSingular')
    Saving _problems/test-analyze_data-33.R
    boundary (singular) fit: see help('isSingular')
    Saving _problems/test-analyze_data-40.R
    boundary (singular) fit: see help('isSingular')
    Saving _problems/test-analyze_data-47.R
    boundary (singular) fit: see help('isSingular')
    Saving _problems/test-analyze_data-78.R
    boundary (singular) fit: see help('isSingular')
    Saving _problems/test-analyze_data-88.R
    Stepped wedge dataset loaded. Discrete time design with 2 clusters, 2 sequences, and 3 time points.
    Stepped wedge dataset loaded. Discrete time design with 2 clusters, 2 sequences, and 3 time points.
    [ FAIL 5 | WARN 0 | SKIP 0 | PASS 24 ]
    
    ══ Failed tests ════════════════════════════════════════════════════════════════
    ── Error ('test-analyze_data.R:32:3'): Correct model type and estimand_type for IT mixed model ──
    Error in `h(simpleError(msg, call))`: error in evaluating the argument 'a' in selecting a method for function 'solve': object 'Hessian' not found
    Backtrace:
         ▆
      1. ├─steppedwedge::analyze(...) at test-analyze_data.R:32:3
      2. │ └─performance::check_convergence(model_it_mixed)
      3. │   ├─insight::is_converged(x, tolerance = tolerance, ...)
      4. │   └─insight:::is_converged.merMod(x, tolerance = tolerance, ...)
      5. │     ├─base::with(x@optinfo$derivs, Matrix::solve(Hessian, gradient))
      6. │     ├─base::with.default(x@optinfo$derivs, Matrix::solve(Hessian, gradient))
      7. │     │ └─base::eval(substitute(expr), data, enclos = parent.frame())
      8. │     │   └─base::eval(substitute(expr), data, enclos = parent.frame())
      9. │     └─Matrix::solve(Hessian, gradient)
     10. └─base::.handleSimpleError(...)
     11.   └─base (local) h(simpleError(msg, call))
    ── Error ('test-analyze_data.R:39:3'): Correct model type and estimand_type for ETI mixed model, TATE ──
    Error in `h(simpleError(msg, call))`: error in evaluating the argument 'a' in selecting a method for function 'solve': object 'Hessian' not found
    Backtrace:
         ▆
      1. ├─steppedwedge::analyze(...) at test-analyze_data.R:39:3
      2. │ └─performance::check_convergence(model_eti_mixed)
      3. │   ├─insight::is_converged(x, tolerance = tolerance, ...)
      4. │   └─insight:::is_converged.merMod(x, tolerance = tolerance, ...)
      5. │     ├─base::with(x@optinfo$derivs, Matrix::solve(Hessian, gradient))
      6. │     ├─base::with.default(x@optinfo$derivs, Matrix::solve(Hessian, gradient))
      7. │     │ └─base::eval(substitute(expr), data, enclos = parent.frame())
      8. │     │   └─base::eval(substitute(expr), data, enclos = parent.frame())
      9. │     └─Matrix::solve(Hessian, gradient)
     10. └─base::.handleSimpleError(...)
     11.   └─base (local) h(simpleError(msg, call))
    ── Error ('test-analyze_data.R:46:3'): Correct model type and estimand_type for ETI mixed model, PTE ──
    Error in `h(simpleError(msg, call))`: error in evaluating the argument 'a' in selecting a method for function 'solve': object 'Hessian' not found
    Backtrace:
         ▆
      1. ├─steppedwedge::analyze(...) at test-analyze_data.R:46:3
      2. │ └─performance::check_convergence(model_eti_mixed)
      3. │   ├─insight::is_converged(x, tolerance = tolerance, ...)
      4. │   └─insight:::is_converged.merMod(x, tolerance = tolerance, ...)
      5. │     ├─base::with(x@optinfo$derivs, Matrix::solve(Hessian, gradient))
      6. │     ├─base::with.default(x@optinfo$derivs, Matrix::solve(Hessian, gradient))
      7. │     │ └─base::eval(substitute(expr), data, enclos = parent.frame())
      8. │     │   └─base::eval(substitute(expr), data, enclos = parent.frame())
      9. │     └─Matrix::solve(Hessian, gradient)
     10. └─base::.handleSimpleError(...)
     11.   └─base (local) h(simpleError(msg, call))
    ── Error ('test-analyze_data.R:77:3'): Model coefficients are returned correctly ──
    Error in `h(simpleError(msg, call))`: error in evaluating the argument 'a' in selecting a method for function 'solve': object 'Hessian' not found
    Backtrace:
         ▆
      1. ├─steppedwedge::analyze(...) at test-analyze_data.R:77:3
      2. │ └─performance::check_convergence(model_it_mixed)
      3. │   ├─insight::is_converged(x, tolerance = tolerance, ...)
      4. │   └─insight:::is_converged.merMod(x, tolerance = tolerance, ...)
      5. │     ├─base::with(x@optinfo$derivs, Matrix::solve(Hessian, gradient))
      6. │     ├─base::with.default(x@optinfo$derivs, Matrix::solve(Hessian, gradient))
      7. │     │ └─base::eval(substitute(expr), data, enclos = parent.frame())
      8. │     │   └─base::eval(substitute(expr), data, enclos = parent.frame())
      9. │     └─Matrix::solve(Hessian, gradient)
     10. └─base::.handleSimpleError(...)
     11.   └─base (local) h(simpleError(msg, call))
    ── Error ('test-analyze_data.R:87:3'): Function handles different family and link functions ──
    Error in `h(simpleError(msg, call))`: error in evaluating the argument 'a' in selecting a method for function 'solve': object 'Hessian' not found
    Backtrace:
         ▆
      1. ├─steppedwedge::analyze(...) at test-analyze_data.R:87:3
      2. │ └─performance::check_convergence(model_it_mixed)
      3. │   ├─insight::is_converged(x, tolerance = tolerance, ...)
      4. │   └─insight:::is_converged.merMod(x, tolerance = tolerance, ...)
      5. │     ├─base::with(x@optinfo$derivs, Matrix::solve(Hessian, gradient))
      6. │     ├─base::with.default(x@optinfo$derivs, Matrix::solve(Hessian, gradient))
      7. │     │ └─base::eval(substitute(expr), data, enclos = parent.frame())
      8. │     │   └─base::eval(substitute(expr), data, enclos = parent.frame())
      9. │     └─Matrix::solve(Hessian, gradient)
     10. └─base::.handleSimpleError(...)
     11.   └─base (local) h(simpleError(msg, call))
    
    [ FAIL 5 | WARN 0 | SKIP 0 | PASS 24 ]
    Error:
    ! Test failures.
    Execution halted

Package: trouBBlme4SolveR
Check: re-building of vignette outputs
New result: ERROR
  Error(s) in re-building vignettes:
    ...
  --- re-building ‘introduction.Rnw’ using Sweave
  Loading required package: Matrix
  Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv,  :
    Model failed to converge with max|grad| = 0.0964374 (tol = 0.002, component 1)
    See ?lme4::convergence and ?lme4::troubleshooting.
  Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv,  :
    Model is nearly unidentifiable: very large eigenvalue
   - Rescale variables?;Model is nearly unidentifiable: large eigenvalue ratio
   - Rescale variables?
  
  Correlation matrix not shown by default, as p = 16 > 12.
  Use print(res$value, correlation=TRUE)  or
      vcov(res$value)        if you need it
  
  Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv,  :
    Model failed to converge with max|grad| = 0.13305 (tol = 0.002, component 1)
    See ?lme4::convergence and ?lme4::troubleshooting.
  boundary (singular) fit: see help('isSingular')
  
  Correlation matrix not shown by default, as p = 16 > 12.
  Use print(res$value, correlation=TRUE)  or
      vcov(res$value)        if you need it
  
  Numeric predictors rescaled!!!
  The default multilevel  model is singular since the between-Day variance for the intercept and the between-SUR.ID variance for the intercepts are zero.
  Then, we consider the next model after removing these random effects.
  
  
  
  Correlation matrix not shown by default, as p = 16 > 12.
  Use print(res$value, correlation=TRUE)  or
      vcov(res$value)        if you need it
  
  boundary (singular) fit: see help('isSingular')
  The default multilevel  model is singular since the between-Subject variance for the nsexage slope is zero.
  Then, we consider the next model after removing this random effect.
  
  
  boundary (singular) fit: see help('isSingular')
  The default multilevel  model is singular since all the random-effects variances are zero. 
  Then, we consider the next model after removing the random effects.
  
  
  Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv,  :
    Model is nearly unidentifiable: very large eigenvalue
   - Rescale variables?
  Numeric predictors rescaled!!!
  
  
  Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv,  :
    Model failed to converge with max|grad| = 0.00276091 (tol = 0.002, component 1)
    See ?lme4::convergence and ?lme4::troubleshooting.
  
  Loading required namespace: ggplot2
  Warning: Some predictor variables are on very different scales: consider rescaling. 
  You may also use (g)lmerControl(autoscale = TRUE) to improve numerical stability.
  
  Error: processing vignette 'introduction.Rnw' failed with diagnostics:
   chunk 11 
  Error in dwmw(fit_1, scale = TRUE, verbose = TRUE) : 
    Too many iterations!! to get the model carat ~ depth + table + price + x + y + z + (1 + price | cut) to converge. Check it!!
  
  --- failed re-building ‘introduction.Rnw’
  
  SUMMARY: processing the following file failed:
    ‘introduction.Rnw’
  
  Error: Vignette re-building failed.
  Execution halted