Running 'testthat.R' [59s]
Running the tests in 'tests/testthat.R' failed.
Complete output:
  > library(testthat)
  > library(PLNmodels)
  > 
  > 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!
  -- 1. Failure: PLN: Check consistency of initialization - fully parametrized cov
  model2$model_par not equal to model1$model_par.
  Component "Theta": Mean relative difference: 0.0002442464
  
  
   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 30 PLN with sparse inverse covariance estimation
  	Joint optimization alternating gradient descent and graphical-lasso
  	sparsifying penalty = 7.297872 
  	sparsifying penalty = 6.740832 
  	sparsifying penalty = 6.22631 
  	sparsifying penalty = 5.751061 
  	sparsifying penalty = 5.312087 
  	sparsifying penalty = 4.90662 
  	sparsifying penalty = 4.532102 
  	sparsifying penalty = 4.186171 
  	sparsifying penalty = 3.866644 
  	sparsifying penalty = 3.571506 
  	sparsifying penalty = 3.298896 
  	sparsifying penalty = 3.047094 
  	sparsifying penalty = 2.814512 
  	sparsifying penalty = 2.599683 
  	sparsifying penalty = 2.401251 
  	sparsifying penalty = 2.217966 
  	sparsifying penalty = 2.04867 
  	sparsifying penalty = 1.892297 
  	sparsifying penalty = 1.74786 
  	sparsifying penalty = 1.614447 
  	sparsifying penalty = 1.491218 
  	sparsifying penalty = 1.377394 
  	sparsifying penalty = 1.272259 
  	sparsifying penalty = 1.175149 
  	sparsifying penalty = 1.085451 
  	sparsifying penalty = 1.002599 
  	sparsifying penalty = 0.9260715 
  	sparsifying penalty = 0.8553852 
  	sparsifying penalty = 0.7900944 
  	sparsifying penalty = 0.7297872 
   Post-treatments
   DONE!
  	sparsifying penalty = 7.297872 
  	sparsifying penalty = 6.740832 
  	sparsifying penalty = 6.22631 
  	sparsifying penalty = 5.751061 
  	sparsifying penalty = 5.312087 
  	sparsifying penalty = 4.90662 
  	sparsifying penalty = 4.532102 
  	sparsifying penalty = 4.186171 
  	sparsifying penalty = 3.866644 
  	sparsifying penalty = 3.571506 
  	sparsifying penalty = 3.298896 
  	sparsifying penalty = 3.047094 
  	sparsifying penalty = 2.814512 
  	sparsifying penalty = 2.599683 
  	sparsifying penalty = 2.401251 
  	sparsifying penalty = 2.217966 
  	sparsifying penalty = 2.04867 
  	sparsifying penalty = 1.892297 
  	sparsifying penalty = 1.74786 
  	sparsifying penalty = 1.614447 
  	sparsifying penalty = 1.491218 
  	sparsifying penalty = 1.377394 
  	sparsifying penalty = 1.272259 
  	sparsifying penalty = 1.175149 
  	sparsifying penalty = 1.085451 
  	sparsifying penalty = 1.002599 
  	sparsifying penalty = 0.9260715 
  	sparsifying penalty = 0.8553852 
  	sparsifying penalty = 0.7900944 
  	sparsifying penalty = 0.7297872 
  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 = 7.297872 
  	sparsifying penalty = 6.740832 
  	sparsifying penalty = 6.22631 
  	sparsifying penalty = 5.751061 
  	sparsifying penalty = 5.312087 
  	sparsifying penalty = 4.90662 
  	sparsifying penalty = 4.532102 
  	sparsifying penalty = 4.186171 
  	sparsifying penalty = 3.866644 
  	sparsifying penalty = 3.571506 
  	sparsifying penalty = 3.298896 
  	sparsifying penalty = 3.047094 
  	sparsifying penalty = 2.814512 
  	sparsifying penalty = 2.599683 
  	sparsifying penalty = 2.401251 
  	sparsifying penalty = 2.217966 
  	sparsifying penalty = 2.04867 
  	sparsifying penalty = 1.892297 
  	sparsifying penalty = 1.74786 
  	sparsifying penalty = 1.614447 
  	sparsifying penalty = 1.491218 
  	sparsifying penalty = 1.377394 
  	sparsifying penalty = 1.272259 
  	sparsifying penalty = 1.175149 
  	sparsifying penalty = 1.085451 
  	sparsifying penalty = 1.002599 
  	sparsifying penalty = 0.9260715 
  	sparsifying penalty = 0.8553852 
  	sparsifying penalty = 0.7900944 
  	sparsifying penalty = 0.7297872 
   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 = 7.297872 
  	sparsifying penalty = 6.740832 
  	sparsifying penalty = 6.22631 
  	sparsifying penalty = 5.751061 
  	sparsifying penalty = 5.312087 
  	sparsifying penalty = 4.90662 
  	sparsifying penalty = 4.532102 
  	sparsifying penalty = 4.186171 
  	sparsifying penalty = 3.866644 
  	sparsifying penalty = 3.571506 
  	sparsifying penalty = 3.298896 
  	sparsifying penalty = 3.047094 
  	sparsifying penalty = 2.814512 
  	sparsifying penalty = 2.599683 
  	sparsifying penalty = 2.401251 
  	sparsifying penalty = 2.217966 
  	sparsifying penalty = 2.04867 
  	sparsifying penalty = 1.892297 
  	sparsifying penalty = 1.74786 
  	sparsifying penalty = 1.614447 
  	sparsifying penalty = 1.491218 
  	sparsifying penalty = 1.377394 
  	sparsifying penalty = 1.272259 
  	sparsifying penalty = 1.175149 
  	sparsifying penalty = 1.085451 
  	sparsifying penalty = 1.002599 
  	sparsifying penalty = 0.9260715 
  	sparsifying penalty = 0.8553852 
  	sparsifying penalty = 0.7900944 
  	sparsifying penalty = 0.7297872 
   Post-treatments
   DONE!
  	 Rank approximation = 1 
  	 Rank approximation = 2 
  	 Rank approximation = 3 
  	 Rank approximation = 4 
  	 Rank approximation = 5 
   Initialization...
  
   Adjusting 5 PLN models for PCA analysis.
  	 Rank approximation = 1 
  	 Rank approximation = 2 
  	 Rank approximation = 3 
  	 Rank approximation = 4 
  	 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!
  == testthat results  ===========================================================
  [ OK: 337 | SKIPPED: 0 | WARNINGS: 4 | FAILED: 1 ]
  1. Failure: PLN: Check consistency of initialization - fully parametrized covariance (@test-pln.R#28) 
  
  Error: testthat unit tests failed
  Execution halted