Running ‘testthat.R’ [75s/115s]
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
  > library(testthat)
  > library(PLNmodels)
  This is packages 'PLNmodels' version 1.0.3
  Use future::plan(multicore/multisession) to speed up PLNPCA/PLNmixture/stability_selection.
  > 
  > test_check("PLNmodels")
  
   Initialization...
   Adjusting a full covariance PLN model with nlopt optimizer
   Post-treatments...
   DONE!
  
   Initialization...
   Adjusting a full covariance PLN model with nlopt optimizer
   Post-treatments...
   DONE!
  
   Initialization...
   Adjusting a full covariance PLN model with nlopt optimizer
   Post-treatments...
   DONE!
  
   Initialization...
   Adjusting a full covariance PLN model with nlopt optimizer
   Post-treatments...
   DONE!
  
   Initialization...
   Adjusting a full covariance PLN model with nlopt optimizer
   Post-treatments...
   DONE!
  
   Initialization...
   Adjusting a full covariance PLN model with nlopt optimizer
   Post-treatments...
   DONE!
  
   Initialization...
   Adjusting a full covariance PLN model with nlopt optimizer
   Post-treatments...
   DONE!
  
   Initialization...
   Adjusting a full covariance PLN model with nlopt optimizer
   Post-treatments...
   DONE!
  
   Initialization...
   Adjusting a full covariance PLN model with nlopt optimizer
   Post-treatments...
   DONE!
  
   Initialization...
   Adjusting a full covariance PLN model with nlopt optimizer
   Post-treatments...
   DONE!
  
   Initialization...
   Adjusting a full covariance PLN model with nlopt optimizer
   Post-treatments...
   DONE!
  
   Initialization...
   Adjusting a full covariance PLN model with nlopt optimizer
   Post-treatments...
   DONE!
  
   Initialization...
   Adjusting a full covariance PLN model with nlopt optimizer
   Post-treatments...
   DONE!
  
   Initialization...
   Adjusting a full covariance PLN model with nlopt optimizer
   Post-treatments...
   DONE!
  
   Initialization...
   Adjusting a full covariance PLN model with nlopt optimizer
   Post-treatments...
   DONE!
  
   Initialization...
   Adjusting a full covariance PLN model with nlopt optimizer
   Post-treatments...
   DONE!
  
   Initialization...
   Adjusting a full covariance PLN model with nlopt optimizer
   Post-treatments...
   DONE!
  
   Initialization...
   Adjusting a full covariance PLN model with nlopt optimizer
   Post-treatments...
   DONE!
  
   Initialization...
   Adjusting a full covariance PLN model with nlopt optimizer
   Post-treatments...
   DONE!
  
   Initialization...
   Adjusting a full covariance PLN model with nlopt optimizer
   Post-treatments...
   DONE!
  
   Initialization...
   Adjusting a spherical covariance PLN model with nlopt optimizer
   Post-treatments...
   DONE!
  
   Initialization...
   Adjusting a diagonal covariance PLN model with nlopt optimizer
   Post-treatments...
   DONE!
  
   Initialization...
   Adjusting a fixed covariance PLN model with nlopt optimizer
   Post-treatments...
   DONE!
  
   Performing discriminant Analysis...
   DONE!
  
   Performing discriminant Analysis...
   DONE!
  
   Initialization...
   Adjusting a full covariance PLN model with nlopt optimizer
   Post-treatments...
   DONE!
  
   Initialization...
   Adjusting a full covariance PLN model with nlopt optimizer
   Post-treatments...
   DONE!
  
   Initialization...
   Adjusting a full covariance PLN model with nlopt optimizer
   Post-treatments...
   DONE!
  
   Initialization...
   Adjusting a diagonal covariance PLN model with nlopt optimizer
   Post-treatments...
   DONE!
  
   Initialization...
   Adjusting a spherical covariance PLN model with nlopt optimizer
   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 
   Post-treatments
   DONE!
  
   Initialization...
  
   Adjusting 3 PLN mixture models.
  	number of cluster = 1 
  	number of cluster = 2 
  	number of cluster = 3 
   Post-treatments
   DONE!
  
   Initialization...
  
   Adjusting 3 PLN mixture models.
  	number of cluster = 1 
  	number of cluster = 2 
  	number of cluster = 3 
   Post-treatments
   DONE!
  
   Initialization...
  
   Adjusting 3 PLN mixture models.
  	number of cluster = 1 
  	number of cluster = 2 
  	number of cluster = 3 
   Post-treatments
   DONE!
  
   Initialization...
  
   Adjusting 3 PLN mixture models.
  	number of cluster = 1 
  	number of cluster = 2 
  	number of cluster = 3 
   Post-treatments
   DONE!
  
   Initialization...
  
   Adjusting 2 PLN mixture models.
  	number of cluster = 2 
  	number of cluster = 4 
  
   Smoothing PLN mixture models.
     Going backward +
                                                                                                      
     Going forward +
                                                                                                      
   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 
   Post-treatments
   DONE!
  
   Initialization...
  
   Adjusting 3 PLN mixture models.
  	number of cluster = 1 
  	number of cluster = 2 
  	number of cluster = 3 
   Post-treatments
   DONE!
  
   Initialization...
  
   Adjusting 3 PLN mixture models.
  	number of cluster = 1 
  	number of cluster = 2 
  	number of cluster = 3 
   Post-treatments
   DONE!
  
   Initialization...
   Adjusting 30 PLN with sparse inverse covariance estimation
  	Joint optimization alternating gradient descent and graphical-lasso
  	sparsifying penalty = 1.932536 
  	sparsifying penalty = 1.785027 
  	sparsifying penalty = 1.648777 
  	sparsifying penalty = 1.522928 
  	sparsifying penalty = 1.406684 
  	sparsifying penalty = 1.299313 
  	sparsifying penalty = 1.200137 
  	sparsifying penalty = 1.108532 
  	sparsifying penalty = 1.023919 
  	sparsifying penalty = 0.9457638 
  	sparsifying penalty = 0.8735745 
  	sparsifying penalty = 0.8068953 
  	sparsifying penalty = 0.7453057 
  	sparsifying penalty = 0.6884171 
  	sparsifying penalty = 0.6358708 
  	sparsifying penalty = 0.5873354 
  	sparsifying penalty = 0.5425046 
  	sparsifying penalty = 0.5010956 
  	sparsifying penalty = 0.4628474 
  	sparsifying penalty = 0.4275187 
  	sparsifying penalty = 0.3948866 
  	sparsifying penalty = 0.3647452 
  	sparsifying penalty = 0.3369045 
  	sparsifying penalty = 0.3111889 
  	sparsifying penalty = 0.2874361 
  	sparsifying penalty = 0.2654964 
  	sparsifying penalty = 0.2452312 
  	sparsifying penalty = 0.226513 
  	sparsifying penalty = 0.2092234 
  	sparsifying penalty = 0.1932536 
   Post-treatments
   DONE!
  
  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 = 1.932536 
  	sparsifying penalty = 1.785027 
  	sparsifying penalty = 1.648777 
  	sparsifying penalty = 1.522928 
  	sparsifying penalty = 1.406684 
  	sparsifying penalty = 1.299313 
  	sparsifying penalty = 1.200137 
  	sparsifying penalty = 1.108532 
  	sparsifying penalty = 1.023919 
  	sparsifying penalty = 0.9457638 
  	sparsifying penalty = 0.8735745 
  	sparsifying penalty = 0.8068953 
  	sparsifying penalty = 0.7453057 
  	sparsifying penalty = 0.6884171 
  	sparsifying penalty = 0.6358708 
  	sparsifying penalty = 0.5873354 
  	sparsifying penalty = 0.5425046 
  	sparsifying penalty = 0.5010956 
  	sparsifying penalty = 0.4628474 
  	sparsifying penalty = 0.4275187 
  	sparsifying penalty = 0.3948866 
  	sparsifying penalty = 0.3647452 
  	sparsifying penalty = 0.3369045 
  	sparsifying penalty = 0.3111889 
  	sparsifying penalty = 0.2874361 
  	sparsifying penalty = 0.2654964 
  	sparsifying penalty = 0.2452312 
  	sparsifying penalty = 0.226513 
  	sparsifying penalty = 0.2092234 
  	sparsifying penalty = 0.1932536 
   Post-treatments
   DONE!
  
  Stability Selection for PLNnetwork: 
  subsampling: ++
   Initialization...
   Initialization...
   Initialization...
   Initialization...
   Adjusting 30 PLN with sparse inverse covariance estimation
  	Joint optimization alternating gradient descent and graphical-lasso
  	sparsifying penalty = 193.2536 
  	sparsifying penalty = 178.5027 
  	sparsifying penalty = 164.8777 
  	sparsifying penalty = 152.2928 
  	sparsifying penalty = 140.6684 
  	sparsifying penalty = 129.9313 
  	sparsifying penalty = 120.0137 
  	sparsifying penalty = 110.8532 
  	sparsifying penalty = 102.3919 
  	sparsifying penalty = 94.57638 
  	sparsifying penalty = 87.35745 
  	sparsifying penalty = 80.68953 
  	sparsifying penalty = 74.53057 
  	sparsifying penalty = 68.84171 
  	sparsifying penalty = 63.58708 
  	sparsifying penalty = 58.73354 
  	sparsifying penalty = 54.25046 
  	sparsifying penalty = 50.10956 
  	sparsifying penalty = 46.28474 
  	sparsifying penalty = 42.75187 
  	sparsifying penalty = 39.48866 
  	sparsifying penalty = 36.47452 
  	sparsifying penalty = 33.69045 
  	sparsifying penalty = 31.11889 
  	sparsifying penalty = 28.74361 
  	sparsifying penalty = 26.54964 
  	sparsifying penalty = 24.52312 
  	sparsifying penalty = 22.6513 
  	sparsifying penalty = 20.92234 
  	sparsifying penalty = 19.32536 
   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 = 7.435767 
  	sparsifying penalty = 6.868201 
  	sparsifying penalty = 6.343958 
  	sparsifying penalty = 5.859729 
  	sparsifying penalty = 5.412461 
  	sparsifying penalty = 4.999332 
  	sparsifying penalty = 4.617737 
  	sparsifying penalty = 4.265269 
  	sparsifying penalty = 3.939705 
  	sparsifying penalty = 3.638991 
  	sparsifying penalty = 3.36123 
  	sparsifying penalty = 3.10467 
  	sparsifying penalty = 2.867693 
  	sparsifying penalty = 2.648805 
  	sparsifying penalty = 2.446624 
  	sparsifying penalty = 2.259875 
  	sparsifying penalty = 2.087381 
  	sparsifying penalty = 1.928053 
  	sparsifying penalty = 1.780886 
  	sparsifying penalty = 1.644953 
  	sparsifying penalty = 1.519395 
  	sparsifying penalty = 1.403421 
  	sparsifying penalty = 1.296299 
  	sparsifying penalty = 1.197353 
  	sparsifying penalty = 1.10596 
  	sparsifying penalty = 1.021543 
  	sparsifying penalty = 0.9435699 
  	sparsifying penalty = 0.871548 
  	sparsifying penalty = 0.8050235 
  	sparsifying penalty = 0.7435767 
   Post-treatments
   DONE!
  
   *** caught segfault ***
  address 0x55bb00000004, cause 'memory not mapped'
  Fatal error: *** recursive gc invocation
  
  Fatal error: *** recursive gc invocation