Running ‘testthat.R’ [50s/81s]
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 = 1.931322 
  	sparsifying penalty = 1.783906 
  	sparsifying penalty = 1.647742 
  	sparsifying penalty = 1.521971 
  	sparsifying penalty = 1.405801 
  	sparsifying penalty = 1.298497 
  	sparsifying penalty = 1.199384 
  	sparsifying penalty = 1.107836 
  	sparsifying penalty = 1.023276 
  	sparsifying penalty = 0.9451699 
  	sparsifying penalty = 0.8730259 
  	sparsifying penalty = 0.8063886 
  	sparsifying penalty = 0.7448377 
  	sparsifying penalty = 0.6879848 
  	sparsifying penalty = 0.6354715 
  	sparsifying penalty = 0.5869666 
  	sparsifying penalty = 0.5421639 
  	sparsifying penalty = 0.500781 
  	sparsifying penalty = 0.4625568 
  	sparsifying penalty = 0.4272502 
  	sparsifying penalty = 0.3946386 
  	sparsifying penalty = 0.3645162 
  	sparsifying penalty = 0.336693 
  	sparsifying penalty = 0.3109935 
  	sparsifying penalty = 0.2872556 
  	sparsifying penalty = 0.2653296 
  	sparsifying penalty = 0.2450773 
  	sparsifying penalty = 0.2263707 
  	sparsifying penalty = 0.209092 
  	sparsifying penalty = 0.1931322 
   Post-treatments
   DONE!
  	sparsifying penalty = 1.931322 
  	sparsifying penalty = 1.783906 
  	sparsifying penalty = 1.647742 
  	sparsifying penalty = 1.521971 
  	sparsifying penalty = 1.405801 
  	sparsifying penalty = 1.298497 
  	sparsifying penalty = 1.199384 
  	sparsifying penalty = 1.107836 
  	sparsifying penalty = 1.023276 
  	sparsifying penalty = 0.9451699 
  	sparsifying penalty = 0.8730259 
  	sparsifying penalty = 0.8063886 
  	sparsifying penalty = 0.7448377 
  	sparsifying penalty = 0.6879848 
  	sparsifying penalty = 0.6354715 
  	sparsifying penalty = 0.5869666 
  	sparsifying penalty = 0.5421639 
  	sparsifying penalty = 0.500781 
  	sparsifying penalty = 0.4625568 
  	sparsifying penalty = 0.4272502 
  	sparsifying penalty = 0.3946386 
  	sparsifying penalty = 0.3645162 
  	sparsifying penalty = 0.336693 
  	sparsifying penalty = 0.3109935 
  	sparsifying penalty = 0.2872556 
  	sparsifying penalty = 0.2653296 
  	sparsifying penalty = 0.2450773 
  	sparsifying penalty = 0.2263707 
  	sparsifying penalty = 0.209092 
  	sparsifying penalty = 0.1931322 
  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 = 1.931322 
  	sparsifying penalty = 1.783906 
  	sparsifying penalty = 1.647742 
  	sparsifying penalty = 1.521971 
  	sparsifying penalty = 1.405801 
  	sparsifying penalty = 1.298497 
  	sparsifying penalty = 1.199384 
  	sparsifying penalty = 1.107836 
  	sparsifying penalty = 1.023276 
  	sparsifying penalty = 0.9451699 
  	sparsifying penalty = 0.8730259 
  	sparsifying penalty = 0.8063886 
  	sparsifying penalty = 0.7448377 
  	sparsifying penalty = 0.6879848 
  	sparsifying penalty = 0.6354715 
  	sparsifying penalty = 0.5869666 
  	sparsifying penalty = 0.5421639 
  	sparsifying penalty = 0.500781 
  	sparsifying penalty = 0.4625568 
  	sparsifying penalty = 0.4272502 
  	sparsifying penalty = 0.3946386 
  	sparsifying penalty = 0.3645162 
  	sparsifying penalty = 0.336693 
  	sparsifying penalty = 0.3109935 
  	sparsifying penalty = 0.2872556 
  	sparsifying penalty = 0.2653296 
  	sparsifying penalty = 0.2450773 
  	sparsifying penalty = 0.2263707 
  	sparsifying penalty = 0.209092 
  	sparsifying penalty = 0.1931322 
   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 = 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