Running 'testthat.R' [72s/88s]
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 = 6.816502 
  	sparsifying penalty = 6.296205 
  	sparsifying penalty = 5.815621 
  	sparsifying penalty = 5.371719 
  	sparsifying penalty = 4.961701 
  	sparsifying penalty = 4.582978 
  	sparsifying penalty = 4.233164 
  	sparsifying penalty = 3.91005 
  	sparsifying penalty = 3.611599 
  	sparsifying penalty = 3.335929 
  	sparsifying penalty = 3.0813 
  	sparsifying penalty = 2.846107 
  	sparsifying penalty = 2.628866 
  	sparsifying penalty = 2.428207 
  	sparsifying penalty = 2.242864 
  	sparsifying penalty = 2.071668 
  	sparsifying penalty = 1.91354 
  	sparsifying penalty = 1.767481 
  	sparsifying penalty = 1.63257 
  	sparsifying penalty = 1.507958 
  	sparsifying penalty = 1.392857 
  	sparsifying penalty = 1.286541 
  	sparsifying penalty = 1.188341 
  	sparsifying penalty = 1.097636 
  	sparsifying penalty = 1.013854 
  	sparsifying penalty = 0.9364673 
  	sparsifying penalty = 0.8649876 
  	sparsifying penalty = 0.7989638 
  	sparsifying penalty = 0.7379796 
  	sparsifying penalty = 0.6816502 
   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 3 | WARN 0 | SKIP 0 | PASS 669 ]
  
  == 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:
      x
   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)
  -- Failure (test-plnpcafit.R:69:3): PLNPCA fit: check classes, getters and field access --
  ve_results$M not equal to unname(myPLNfit$var_par$M).
  41/196 mismatches (average diff: 0.0205)
  [2]  -1.6059 - -1.6189 ==  0.0130
  [3]  -1.0146 - -1.0340 ==  0.0194
  [4]   0.0971 -  0.0274 ==  0.0697
  [13] -1.2033 - -1.2240 ==  0.0207
  [15]  0.8361 -  0.7866 ==  0.0495
  [16]  0.0899 -  0.0745 ==  0.0154
  [19] -0.0985 - -0.1086 ==  0.0101
  [31]  2.0717 -  2.0823 == -0.0106
  [32]  0.2248 -  0.2075 ==  0.0173
  ...
  -- Failure (test-plnpcafit.R:74:3): PLNPCA fit: check classes, getters and field access --
  `scores` not equal to myPLNfit$scores.
  137/196 mismatches (average diff: 0.0406)
  [1]   -1.665 -  -1.63 == -0.0356
  [2]  -11.339 - -11.38 ==  0.0452
  [3]   -9.605 -  -9.70 ==  0.0905
  [4]   -0.606 -  -1.16 ==  0.5530
  [7]   -7.111 -  -7.09 == -0.0209
  [8]   -2.461 -  -2.44 == -0.0240
  [9]   -7.948 -  -7.93 == -0.0153
  [10]  -9.678 -  -9.70 ==  0.0179
  [12]  -9.780 -  -9.82 ==  0.0353
  ...
  
  [ FAIL 3 | WARN 0 | SKIP 0 | PASS 669 ]
  Error: Test failures
  Execution halted