Running ‘testthat.R’ [50s/50s]
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
  > library(testthat)
  > library(PLNmodels)
  This is packages 'PLNmodels' version 0.11.5
  Use future::plan(multicore/multisession) to speed up PLNPCA/PLNmixture/stability_selection.
  > 
  > test_check("PLNmodels")
  
   Initialization...
   Adjusting a PLN model with full covariance model
   Post-treatments...
   DONE!
  
   Initialization...
   Adjusting a PLN model with full covariance model
   Post-treatments...
   DONE!
  
   Initialization...
   Adjusting a PLN model with full covariance model
   Post-treatments...
   DONE!
  
   Initialization...
   Adjusting a PLN model with full covariance model
   Post-treatments...
   DONE!
  
   Initialization...
   Adjusting a PLN model with full covariance model
   Post-treatments...
   DONE!
  
   Initialization...
   Adjusting a PLN model with full covariance model
   Post-treatments...
   DONE!
  
   Initialization...
   Adjusting a PLN model with full covariance model
   Post-treatments...
   DONE!
  
   Initialization...
   Adjusting a PLN model with full covariance model
   Post-treatments...
   DONE!
  
   Initialization...
   Adjusting a PLN model with full covariance model
   Post-treatments...
   DONE!
  
   Initialization...
   Adjusting a PLN model with full covariance model
   Post-treatments...
   DONE!
  
   Initialization...
   Adjusting a PLN model with full covariance model
   Post-treatments...
   DONE!
  
   Initialization...
   Adjusting a PLN model with full covariance model
   Post-treatments...
   DONE!
  
   Initialization...
   Adjusting a PLN model with full covariance model
   Post-treatments...
   DONE!
  
   Initialization...
   Adjusting a PLN model with full covariance model
   Post-treatments...
   DONE!
  
   Initialization...
   Adjusting a PLN model with full covariance model
   Post-treatments...
   DONE!
  
   Initialization...
   Adjusting a PLN model with full covariance model
   Post-treatments...
   DONE!
  
   Initialization...
   Adjusting a PLN model with full covariance model
   Post-treatments...
   DONE!
  
   Initialization...
   Adjusting a PLN model with full covariance model
   Post-treatments...
   DONE!
  
   Initialization...
   Adjusting a PLN model with full covariance model
   Post-treatments...
   DONE!
  
   Initialization...
   Adjusting a PLN model with diagonal covariance model
   Post-treatments...
   DONE!
  
   Initialization...
   Adjusting a PLN model with spherical covariance model
   Post-treatments...
   DONE!
  
   Performing discriminant Analysis...
   DONE!
  
   Performing discriminant Analysis...
   DONE!
  
   Performing discriminant Analysis...
   DONE!
  
   Performing discriminant Analysis...
   DONE!
  
   Performing discriminant Analysis...
   DONE!
  
   Performing discriminant Analysis...
   DONE!
  
   Performing discriminant Analysis...
   DONE!
  
   Initialization...
  
   Adjusting 3 PLN mixture models.
  	number of cluster = 1 
  	number of cluster = 2 
  	number of cluster = 3 
  
   Smoothing PLN mixture models.
  
   Post-treatments
   DONE!
  
   Initialization...
  
   Adjusting 3 PLN mixture models.
  	number of cluster = 1 
  	number of cluster = 2 
  	number of cluster = 3 
  
   Smoothing PLN mixture models.
  
   Post-treatments
   DONE!
  
   Initialization...
  
   Adjusting 3 PLN mixture models.
  	number of cluster = 1 
  	number of cluster = 2 
  	number of cluster = 3 
  
   Smoothing PLN mixture models.
  
   Post-treatments
   DONE!
  
   Initialization...
  
   Adjusting 3 PLN mixture models.
  	number of cluster = 1 
  	number of cluster = 2 
  	number of cluster = 3 
  
   Smoothing PLN mixture models.
  
   Post-treatments
   DONE!
  
   Initialization...
  
   Adjusting 3 PLN mixture models.
  	number of cluster = 1 
  	number of cluster = 2 
  	number of cluster = 3 
  
   Smoothing PLN mixture models.
  
   Post-treatments
   DONE!
  
   Initialization...
  
   Adjusting 1 PLN mixture models.
  
   Initialization...
  
   Adjusting 3 PLN mixture models.
  	number of cluster = 1 
  	number of cluster = 2 
  	number of cluster = 3 
  
   Smoothing PLN mixture models.
  
   Post-treatments
   DONE!
  
   Initialization...
  
   Adjusting 3 PLN mixture models.
  	number of cluster = 1 
  	number of cluster = 2 
  	number of cluster = 3 
  
   Smoothing PLN mixture models.
  
   Post-treatments
   DONE!
  
   Initialization...
  
   Adjusting 3 PLN mixture models.
  	number of cluster = 1 
  	number of cluster = 2 
  	number of cluster = 3 
  
   Smoothing PLN mixture models.
  
   Post-treatments
   DONE!
  
   Initialization...
   Adjusting 30 PLN with sparse inverse covariance estimation
  	Joint optimization alternating gradient descent and graphical-lasso
  	sparsifying penalty = 1.933227 
  	sparsifying penalty = 1.785665 
  	sparsifying penalty = 1.649367 
  	sparsifying penalty = 1.523472 
  	sparsifying penalty = 1.407187 
  	sparsifying penalty = 1.299778 
  	sparsifying penalty = 1.200567 
  	sparsifying penalty = 1.108928 
  	sparsifying penalty = 1.024285 
  	sparsifying penalty = 0.9461021 
  	sparsifying penalty = 0.873887 
  	sparsifying penalty = 0.8071839 
  	sparsifying penalty = 0.7455723 
  	sparsifying penalty = 0.6886634 
  	sparsifying penalty = 0.6360983 
  	sparsifying penalty = 0.5875455 
  	sparsifying penalty = 0.5426986 
  	sparsifying penalty = 0.5012749 
  	sparsifying penalty = 0.463013 
  	sparsifying penalty = 0.4276716 
  	sparsifying penalty = 0.3950278 
  	sparsifying penalty = 0.3648757 
  	sparsifying penalty = 0.337025 
  	sparsifying penalty = 0.3113002 
  	sparsifying penalty = 0.2875389 
  	sparsifying penalty = 0.2655913 
  	sparsifying penalty = 0.245319 
  	sparsifying penalty = 0.226594 
  	sparsifying penalty = 0.2092983 
  	sparsifying penalty = 0.1933227 
   Post-treatments
   DONE!
  	sparsifying penalty = 1.933227 
  	sparsifying penalty = 1.785665 
  	sparsifying penalty = 1.649367 
  	sparsifying penalty = 1.523472 
  	sparsifying penalty = 1.407187 
  	sparsifying penalty = 1.299778 
  	sparsifying penalty = 1.200567 
  	sparsifying penalty = 1.108928 
  	sparsifying penalty = 1.024285 
  	sparsifying penalty = 0.9461021 
  	sparsifying penalty = 0.873887 
  	sparsifying penalty = 0.8071839 
  	sparsifying penalty = 0.7455723 
  	sparsifying penalty = 0.6886634 
  	sparsifying penalty = 0.6360983 
  	sparsifying penalty = 0.5875455 
  	sparsifying penalty = 0.5426986 
  	sparsifying penalty = 0.5012749 
  	sparsifying penalty = 0.463013 
  	sparsifying penalty = 0.4276716 
  	sparsifying penalty = 0.3950278 
  	sparsifying penalty = 0.3648757 
  	sparsifying penalty = 0.337025 
  	sparsifying penalty = 0.3113002 
  	sparsifying penalty = 0.2875389 
  	sparsifying penalty = 0.2655913 
  	sparsifying penalty = 0.245319 
  	sparsifying penalty = 0.226594 
  	sparsifying penalty = 0.2092983 
  	sparsifying penalty = 0.1933227 
  Stability Selection for PLNnetwork: 
  subsampling: ++
  Stability Selection for PLNnetwork: 
  subsampling: ++
   Initialization...
   Adjusting 30 PLN with sparse inverse covariance estimation
  	Joint optimization alternating gradient descent and graphical-lasso
  	sparsifying penalty = 1.933227 
  	sparsifying penalty = 1.785665 
  	sparsifying penalty = 1.649367 
  	sparsifying penalty = 1.523472 
  	sparsifying penalty = 1.407187 
  	sparsifying penalty = 1.299778 
  	sparsifying penalty = 1.200567 
  	sparsifying penalty = 1.108928 
  	sparsifying penalty = 1.024285 
  	sparsifying penalty = 0.9461021 
  	sparsifying penalty = 0.873887 
  	sparsifying penalty = 0.8071839 
  	sparsifying penalty = 0.7455723 
  	sparsifying penalty = 0.6886634 
  	sparsifying penalty = 0.6360983 
  	sparsifying penalty = 0.5875455 
  	sparsifying penalty = 0.5426986 
  	sparsifying penalty = 0.5012749 
  	sparsifying penalty = 0.463013 
  	sparsifying penalty = 0.4276716 
  	sparsifying penalty = 0.3950278 
  	sparsifying penalty = 0.3648757 
  	sparsifying penalty = 0.337025 
  	sparsifying penalty = 0.3113002 
  	sparsifying penalty = 0.2875389 
  	sparsifying penalty = 0.2655913 
  	sparsifying penalty = 0.245319 
  	sparsifying penalty = 0.226594 
  	sparsifying penalty = 0.2092983 
  	sparsifying penalty = 0.1933227 
   Post-treatments
   DONE!
  
  Stability Selection for PLNnetwork: 
  subsampling: ++
   Initialization...
   Adjusting 30 PLN with sparse inverse covariance estimation
  	Joint optimization alternating gradient descent and graphical-lasso
  	sparsifying penalty = 7.436075 
  	sparsifying penalty = 6.868485 
  	sparsifying penalty = 6.34422 
  	sparsifying penalty = 5.859971 
  	sparsifying penalty = 5.412684 
  	sparsifying penalty = 4.999539 
  	sparsifying penalty = 4.617928 
  	sparsifying penalty = 4.265446 
  	sparsifying penalty = 3.939868 
  	sparsifying penalty = 3.639141 
  	sparsifying penalty = 3.361369 
  	sparsifying penalty = 3.104798 
  	sparsifying penalty = 2.867812 
  	sparsifying penalty = 2.648914 
  	sparsifying penalty = 2.446725 
  	sparsifying penalty = 2.259968 
  	sparsifying penalty = 2.087467 
  	sparsifying penalty = 1.928132 
  	sparsifying penalty = 1.78096 
  	sparsifying penalty = 1.645021 
  	sparsifying penalty = 1.519458 
  	sparsifying penalty = 1.403479 
  	sparsifying penalty = 1.296352 
  	sparsifying penalty = 1.197403 
  	sparsifying penalty = 1.106006 
  	sparsifying penalty = 1.021586 
  	sparsifying penalty = 0.9436089 
  	sparsifying penalty = 0.871584 
  	sparsifying penalty = 0.8050567 
  	sparsifying penalty = 0.7436075 
   Post-treatments
   DONE!
  	 Rank approximation = 5 
  	 Rank approximation = 4 
  	 Rank approximation = 1 
  	 Rank approximation = 3 
  	 Rank approximation = 2 
   Initialization...
  
   Adjusting 5 PLN models for PCA analysis.
  	 Rank approximation = 1 
  	 Rank approximation = 3 
  	 Rank approximation = 4 
  	 Rank approximation = 2 
  	 Rank approximation = 5 
   Post-treatments
   DONE!
  
   Initialization...
   Adjusting a PLN model with full covariance model
   Post-treatments...
   DONE!
  
   Initialization...
   Adjusting a PLN model with full covariance model
   Post-treatments...
   DONE!
  [ FAIL 1 | WARN 0 | SKIP 0 | PASS 671 ]
  
  ══ Failed tests ════════════════════════════════════════════════════════════════
  ── Error (test-pln.R:153:3): PLN: Check that all univariate PLN models are equivalent with the multivariate diagonal case ──
  Error in `all.equal.numeric(map_dbl(univariate_spherical, "nb_param"), 
      map_dbl(univariate_full, "nb_param"), map_dbl(univariate_diagonal, 
          "nb_param"))`: length(tolerance) == 1L is not TRUE
  Backtrace:
      █
   1. ├─testthat::expect_true(...) test-pln.R:153:2
   2. │ └─testthat::quasi_label(enquo(object), label, arg = "object")
   3. │   └─rlang::eval_bare(expr, quo_get_env(quo))
   4. ├─base::all.equal(...)
   5. ├─base::all.equal(...)
   6. └─base::all.equal.numeric(...)
   7.   └─base::stopifnot(length(tolerance) == 1L)
  
  [ FAIL 1 | WARN 0 | SKIP 0 | PASS 671 ]
  Error: Test failures
  Execution halted