Running ‘testthat.R’ [78s/122s]
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
  > library(testthat)
  > library(PLNmodels)
  This is packages 'PLNmodels' version 0.11.4
  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 5 PLN mixture models.
  	number of cluster = 1 
  	number of cluster = 2 
  	number of cluster = 3 
  	number of cluster = 4 
  	number of cluster = 5 
  
   Smoothing PLN mixture models.
  
   Post-treatments
   DONE!
  
   Initialization...
  
   Adjusting 5 PLN mixture models.
  	number of cluster = 1 
  	number of cluster = 2 
  	number of cluster = 3 
  	number of cluster = 4 
  	number of cluster = 5 
  
   Smoothing PLN mixture models.
  
   Post-treatments
   DONE!
  
   Initialization...
  
   Adjusting 5 PLN mixture models.
  	number of cluster = 1 
  	number of cluster = 2 
  	number of cluster = 3 
  	number of cluster = 4 
  	number of cluster = 5 
  
   Smoothing PLN mixture models.
     Going backward ++++
                                                                                                      
     Going forward ++++
                                                                                                      
     Going backward ++++
                                                                                                      
     Going forward ++++
                                                                                                      
   Post-treatments
   DONE!
  
   Initialization...
  
   Adjusting 5 PLN mixture models.
  	number of cluster = 1 
  	number of cluster = 2 
  	number of cluster = 3 
  	number of cluster = 4 
  	number of cluster = 5 
  
   Smoothing PLN mixture models.
  
   Post-treatments
   DONE!
  
   Initialization...
  
   Adjusting 5 PLN mixture models.
  	number of cluster = 1 
  	number of cluster = 2 
  	number of cluster = 3 
  	number of cluster = 4 
  	number of cluster = 5 
  
   Smoothing PLN mixture models.
  
   Post-treatments
   DONE!
  
   Initialization...
  
   Adjusting 1 PLN mixture models.
  
   Initialization...
  
   Adjusting 5 PLN mixture models.
  	number of cluster = 1 
  	number of cluster = 2 
  	number of cluster = 3 
  	number of cluster = 4 
  	number of cluster = 5 
  
   Smoothing PLN mixture models.
     Going backward ++++
                                                                                                      
     Going forward ++++
                                                                                                      
   Post-treatments
   DONE!
  
   Initialization...
  
   Adjusting 5 PLN mixture models.
  	number of cluster = 1 
  	number of cluster = 2 
  	number of cluster = 3 
  	number of cluster = 4 
  	number of cluster = 5 
  
   Smoothing PLN mixture models.
  
   Post-treatments
   DONE!
  
   Initialization...
  
   Adjusting 5 PLN mixture models.
  	number of cluster = 1 
  	number of cluster = 2 
  	number of cluster = 3 
  	number of cluster = 4 
  	number of cluster = 5 
  
   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 = 7.017338 
  	sparsifying penalty = 6.48171 
  	sparsifying penalty = 5.986967 
  	sparsifying penalty = 5.529987 
  	sparsifying penalty = 5.107888 
  	sparsifying penalty = 4.718007 
  	sparsifying penalty = 4.357886 
  	sparsifying penalty = 4.025252 
  	sparsifying penalty = 3.718008 
  	sparsifying penalty = 3.434215 
  	sparsifying penalty = 3.172085 
  	sparsifying penalty = 2.929962 
  	sparsifying penalty = 2.706321 
  	sparsifying penalty = 2.49975 
  	sparsifying penalty = 2.308946 
  	sparsifying penalty = 2.132706 
  	sparsifying penalty = 1.969918 
  	sparsifying penalty = 1.819556 
  	sparsifying penalty = 1.680671 
  	sparsifying penalty = 1.552387 
  	sparsifying penalty = 1.433894 
  	sparsifying penalty = 1.324447 
  	sparsifying penalty = 1.223353 
  	sparsifying penalty = 1.129975 
  	sparsifying penalty = 1.043725 
  	sparsifying penalty = 0.9640585 
  	sparsifying penalty = 0.8904728 
  	sparsifying penalty = 0.8225038 
  	sparsifying penalty = 0.7597227 
  	sparsifying penalty = 0.7017338 
   Post-treatments
   DONE!
  	sparsifying penalty = 7.017338 
  	sparsifying penalty = 6.48171 
  	sparsifying penalty = 5.986967 
  	sparsifying penalty = 5.529987 
  	sparsifying penalty = 5.107888 
  	sparsifying penalty = 4.718007 
  	sparsifying penalty = 4.357886 
  	sparsifying penalty = 4.025252 
  	sparsifying penalty = 3.718008 
  	sparsifying penalty = 3.434215 
  	sparsifying penalty = 3.172085 
  	sparsifying penalty = 2.929962 
  	sparsifying penalty = 2.706321 
  	sparsifying penalty = 2.49975 
  	sparsifying penalty = 2.308946 
  	sparsifying penalty = 2.132706 
  	sparsifying penalty = 1.969918 
  	sparsifying penalty = 1.819556 
  	sparsifying penalty = 1.680671 
  	sparsifying penalty = 1.552387 
  	sparsifying penalty = 1.433894 
  	sparsifying penalty = 1.324447 
  	sparsifying penalty = 1.223353 
  	sparsifying penalty = 1.129975 
  	sparsifying penalty = 1.043725 
  	sparsifying penalty = 0.9640585 
  	sparsifying penalty = 0.8904728 
  	sparsifying penalty = 0.8225038 
  	sparsifying penalty = 0.7597227 
  	sparsifying penalty = 0.7017338 
  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 = 7.017338 
  	sparsifying penalty = 6.48171 
  	sparsifying penalty = 5.986967 
  	sparsifying penalty = 5.529987 
  	sparsifying penalty = 5.107888 
  	sparsifying penalty = 4.718007 
  	sparsifying penalty = 4.357886 
  	sparsifying penalty = 4.025252 
  	sparsifying penalty = 3.718008 
  	sparsifying penalty = 3.434215 
  	sparsifying penalty = 3.172085 
  	sparsifying penalty = 2.929962 
  	sparsifying penalty = 2.706321 
  	sparsifying penalty = 2.49975 
  	sparsifying penalty = 2.308946 
  	sparsifying penalty = 2.132706 
  	sparsifying penalty = 1.969918 
  	sparsifying penalty = 1.819556 
  	sparsifying penalty = 1.680671 
  	sparsifying penalty = 1.552387 
  	sparsifying penalty = 1.433894 
  	sparsifying penalty = 1.324447 
  	sparsifying penalty = 1.223353 
  	sparsifying penalty = 1.129975 
  	sparsifying penalty = 1.043725 
  	sparsifying penalty = 0.9640585 
  	sparsifying penalty = 0.8904728 
  	sparsifying penalty = 0.8225038 
  	sparsifying penalty = 0.7597227 
  	sparsifying penalty = 0.7017338 
   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 = 7.017338 
  	sparsifying penalty = 6.48171 
  	sparsifying penalty = 5.986967 
  	sparsifying penalty = 5.529987 
  	sparsifying penalty = 5.107888 
  	sparsifying penalty = 4.718007 
  	sparsifying penalty = 4.357886 
  	sparsifying penalty = 4.025252 
  	sparsifying penalty = 3.718008 
  	sparsifying penalty = 3.434215 
  	sparsifying penalty = 3.172085 
  	sparsifying penalty = 2.929962 
  	sparsifying penalty = 2.706321 
  	sparsifying penalty = 2.49975 
  	sparsifying penalty = 2.308946 
  	sparsifying penalty = 2.132706 
  	sparsifying penalty = 1.969918 
  	sparsifying penalty = 1.819556 
  	sparsifying penalty = 1.680671 
  	sparsifying penalty = 1.552387 
  	sparsifying penalty = 1.433894 
  	sparsifying penalty = 1.324447 
  	sparsifying penalty = 1.223353 
  	sparsifying penalty = 1.129975 
  	sparsifying penalty = 1.043725 
  	sparsifying penalty = 0.9640585 
  	sparsifying penalty = 0.8904728 
  	sparsifying penalty = 0.8225038 
  	sparsifying penalty = 0.7597227 
  	sparsifying penalty = 0.7017338 
   Post-treatments
   DONE!
  	 Rank approximation = 1 
  	 Rank approximation = 5 
  	 Rank approximation = 2 
  	 Rank approximation = 3 
  	 Rank approximation = 4 
   Initialization...
  
   Adjusting 5 PLN models for PCA analysis.
  	 Rank approximation = 2 
  	 Rank approximation = 5 
  	 Rank approximation = 1 
  	 Rank approximation = 3 
  	 Rank approximation = 4 
   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!
  ══ Failed tests ════════════════════════════════════════════════════════════════
  ── Failure (test-pln.R:174:3): PLN: Check that all univariate PLN models are equivalent with the multivariate diagonal case ──
  all.equal(...) is not TRUE
  
  `actual` is a character vector ('Mean scaled difference: 0.09806209')
  `expected` is a logical vector (TRUE)
  
  [ FAIL 1 | WARN 43 | SKIP 0 | PASS 670 ]
  Error: Test failures
  Execution halted