Running 'testthat.R' [78s]
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 = 1.931518 
  	sparsifying penalty = 1.784087 
  	sparsifying penalty = 1.647909 
  	sparsifying penalty = 1.522126 
  	sparsifying penalty = 1.405943 
  	sparsifying penalty = 1.298629 
  	sparsifying penalty = 1.199506 
  	sparsifying penalty = 1.107948 
  	sparsifying penalty = 1.02338 
  	sparsifying penalty = 0.945266 
  	sparsifying penalty = 0.8731146 
  	sparsifying penalty = 0.8064705 
  	sparsifying penalty = 0.7449133 
  	sparsifying penalty = 0.6880547 
  	sparsifying penalty = 0.6355361 
  	sparsifying penalty = 0.5870262 
  	sparsifying penalty = 0.542219 
  	sparsifying penalty = 0.5008319 
  	sparsifying penalty = 0.4626038 
  	sparsifying penalty = 0.4272936 
  	sparsifying penalty = 0.3946787 
  	sparsifying penalty = 0.3645532 
  	sparsifying penalty = 0.3367272 
  	sparsifying penalty = 0.3110251 
  	sparsifying penalty = 0.2872848 
  	sparsifying penalty = 0.2653566 
  	sparsifying penalty = 0.2451022 
  	sparsifying penalty = 0.2263937 
  	sparsifying penalty = 0.2091133 
  	sparsifying penalty = 0.1931518 
   Post-treatments
   DONE!
  	sparsifying penalty = 1.931518 
  	sparsifying penalty = 1.784087 
  	sparsifying penalty = 1.647909 
  	sparsifying penalty = 1.522126 
  	sparsifying penalty = 1.405943 
  	sparsifying penalty = 1.298629 
  	sparsifying penalty = 1.199506 
  	sparsifying penalty = 1.107948 
  	sparsifying penalty = 1.02338 
  	sparsifying penalty = 0.945266 
  	sparsifying penalty = 0.8731146 
  	sparsifying penalty = 0.8064705 
  	sparsifying penalty = 0.7449133 
  	sparsifying penalty = 0.6880547 
  	sparsifying penalty = 0.6355361 
  	sparsifying penalty = 0.5870262 
  	sparsifying penalty = 0.542219 
  	sparsifying penalty = 0.5008319 
  	sparsifying penalty = 0.4626038 
  	sparsifying penalty = 0.4272936 
  	sparsifying penalty = 0.3946787 
  	sparsifying penalty = 0.3645532 
  	sparsifying penalty = 0.3367272 
  	sparsifying penalty = 0.3110251 
  	sparsifying penalty = 0.2872848 
  	sparsifying penalty = 0.2653566 
  	sparsifying penalty = 0.2451022 
  	sparsifying penalty = 0.2263937 
  	sparsifying penalty = 0.2091133 
  	sparsifying penalty = 0.1931518 
  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 = 1.931518 
  	sparsifying penalty = 1.784087 
  	sparsifying penalty = 1.647909 
  	sparsifying penalty = 1.522126 
  	sparsifying penalty = 1.405943 
  	sparsifying penalty = 1.298629 
  	sparsifying penalty = 1.199506 
  	sparsifying penalty = 1.107948 
  	sparsifying penalty = 1.02338 
  	sparsifying penalty = 0.945266 
  	sparsifying penalty = 0.8731146 
  	sparsifying penalty = 0.8064705 
  	sparsifying penalty = 0.7449133 
  	sparsifying penalty = 0.6880547 
  	sparsifying penalty = 0.6355361 
  	sparsifying penalty = 0.5870262 
  	sparsifying penalty = 0.542219 
  	sparsifying penalty = 0.5008319 
  	sparsifying penalty = 0.4626038 
  	sparsifying penalty = 0.4272936 
  	sparsifying penalty = 0.3946787 
  	sparsifying penalty = 0.3645532 
  	sparsifying penalty = 0.3367272 
  	sparsifying penalty = 0.3110251 
  	sparsifying penalty = 0.2872848 
  	sparsifying penalty = 0.2653566 
  	sparsifying penalty = 0.2451022 
  	sparsifying penalty = 0.2263937 
  	sparsifying penalty = 0.2091133 
  	sparsifying penalty = 0.1931518 
   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 = 7.351355 
  	sparsifying penalty = 6.790233 
  	sparsifying penalty = 6.27194 
  	sparsifying penalty = 5.793208 
  	sparsifying penalty = 5.351018 
  	sparsifying penalty = 4.942579 
  	sparsifying penalty = 4.565316 
  	sparsifying penalty = 4.21685 
  	sparsifying penalty = 3.894981 
  	sparsifying penalty = 3.59768 
  	sparsifying penalty = 3.323073 
  	sparsifying penalty = 3.069425 
  	sparsifying penalty = 2.835139 
  	sparsifying penalty = 2.618735 
  	sparsifying penalty = 2.418849 
  	sparsifying penalty = 2.234221 
  	sparsifying penalty = 2.063684 
  	sparsifying penalty = 1.906165 
  	sparsifying penalty = 1.760669 
  	sparsifying penalty = 1.626279 
  	sparsifying penalty = 1.502146 
  	sparsifying penalty = 1.387489 
  	sparsifying penalty = 1.281583 
  	sparsifying penalty = 1.183761 
  	sparsifying penalty = 1.093405 
  	sparsifying penalty = 1.009947 
  	sparsifying penalty = 0.9328583 
  	sparsifying penalty = 0.861654 
  	sparsifying penalty = 0.7958847 
  	sparsifying penalty = 0.7351355 
   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