Running 'spelling.R' [0s]
  Running 'testthat.R' [122s]
Running the tests in 'tests/testthat.R' failed.
Complete output:
  > library(testthat)
  > library(missSBM)
  > 
  > test_check("missSBM")
  
   Adjusting Variational EM for Stochastic Block Model
  
  	Dyads are distributed according to a 'undirected' SBM.
  
  	Imputation assumes a 'covar-dyad' network-sampling process
   iteration #: 2 
   iteration #: 3 
  
   Adjusting Variational EM for Stochastic Block Model
  
  	Dyads are distributed according to a 'undirected' SBM.
  
  	Imputation assumes a 'dyad' network-sampling process
   iteration #: 2 
   iteration #: 3 
  
   Adjusting Variational EM for Stochastic Block Model
  
  	Dyads are distributed according to a 'undirected' SBM.
  
  	Imputation assumes a 'covar-node' network-sampling process
   iteration #: 2 
   iteration #: 3 
  
   Adjusting Variational EM for Stochastic Block Model
  
  	Dyads are distributed according to a 'undirected' SBM.
  
  	Imputation assumes a 'node' network-sampling process
   iteration #: 2 
   iteration #: 3 
  Tested sampling:
   - dyad
   - node
   - double-standard
   - block-node
   - block-dyad
  
   Adjusting Variational EM for Stochastic Block Model
  
  	Imputation assumes a 'dyad' network-sampling process
  
   Initialization of 3 model(s). 
  Tested sampling:
   - dyad
   - node
   - double-standard
   - block-node
   - block-dyadTested sampling:
   - dyad
   - node
   - double-standard
   - block-node
   - block-dyad
  sampling: dyad new better on connectivity
  node new better on mixture new better on connectivity
  double-standard new better on mixture
  block-node
  
   Adjusting Variational EM for Stochastic Block Model
   iteration #: 2 
  
   Adjusting Variational EM for Stochastic Block Model
  
  	Dyads are distributed according to a 'undirected' SBM.
  
  	Imputation assumes a 'node' network-sampling process
   iteration #: 2 
  
  
   Adjusting Variational EM for Stochastic Block Model
  
  	Imputation assumes a 'node' network-sampling process
  
   Initialization of 1 model(s). 
   Performing VEM inference
   	Model with 3 blocks.
  
  
  
  error: arma::memory::acquire(): out of memory
  
  This application has requested the Runtime to terminate it in an unusual way.
  Please contact the application's support team for more information.
  terminate called after throwing an instance of 'std::bad_alloc'
    what():  std::bad_alloc