Running 'BM_bernoulli.R' [1s]
  Running 'BM_bernoulli_covariates.R' [1s]
  Running 'BM_bernoulli_covariates_fast.R' [1s]
  Running 'BM_bernoulli_multiplex.R' [1s]
  Running 'BM_gaussian.R' [1s]
  Running 'BM_gaussian_covariates.R' [161s]
  Running 'BM_gaussian_multivariate.R' [1s]
  Running 'BM_gaussian_multivariate_independent.R' [1s]
  Running 'BM_gaussian_multivariate_independent_homoscedastic.R' [159s]
  Running 'BM_poisson.R' [1s]
  Running 'BM_poisson_covariates.R' [1s]
Running the tests in 'tests/BM_gaussian_covariates.R' failed.
Complete output:
  > require('blockmodels')
  Loading required package: blockmodels
  > set.seed(12)
  > ##
  > ## SBM
  > ##
  > 
  > ## generation of one SBM network
  > npc <- 10 # nodes per class
  > Q <- 2 # classes
  > n <- npc * Q # nodes
  > Z<-diag(Q)%x%matrix(1,npc,1)
  > Mu<-20*matrix(runif(Q*Q),Q,Q)
  > Y1 <- matrix(runif(n*n),n,n)
  > Y2 <- matrix(runif(n*n),n,n)
  > M<-matrix(rnorm(n*n,sd=5),n,n)+Z%*%Mu%*%t(Z)+4.2*Y1-1.6*Y2 ## adjacency matrix
  > 
  > ## estimation
  > my_model <- BM_gaussian_covariates("SBM",M,list(Y1,Y2) , plotting='', explore_min=2, explore_max=2, ncores=2, verbosity=0)
  > my_model$estimate()
  
  
  > which.max(my_model$ICL)
  [1] 2
  > 
  > ##
  > ## SBM symmetric
  > ##
  > 
  > ## generation of one SBM_sym network
  > npc <- 10 # nodes per class
  > Q <- 2 # classes
  > n <- npc * Q # nodes
  > Z<-diag(Q)%x%matrix(1,npc,1)
  > Mu<-20*matrix(runif(Q*Q),Q,Q)
  > Mu[lower.tri(Mu)]<-t(Mu)[lower.tri(Mu)]
  > Y1 <- matrix(runif(n*n),n,n)
  > Y2 <- matrix(runif(n*n),n,n)
  > Y1[lower.tri(Y1)]<-t(Y1)[lower.tri(Y1)]
  > Y2[lower.tri(Y2)]<-t(Y2)[lower.tri(Y2)]
  > M<-matrix(rnorm(n*n,sd=5),n,n)+Z%*%Mu%*%t(Z)+4.2*Y1-1.6*Y2 ## adjacency matrix
  > M[lower.tri(M)]<-t(M)[lower.tri(M)]
  > 
  > ## estimation
  > my_model <- BM_gaussian_covariates("SBM_sym",M,list(Y1,Y2) , plotting='', explore_min=2, explore_max=2, ncores=2, verbosity=0)
  > my_model$estimate()
  
  
  > which.max(my_model$ICL)
  [1] 2
  > 
  > ##
  > ## LBM
  > ##
  > 
  > ## generation of one LBM network
  > npc <- c(20,10) # nodes per class
  > Q <- c(1,2) # classes
  > n <- npc * Q # nodes
  > Z1<-diag(Q[1])%x%matrix(1,npc[1],1)
  > Z2<-diag(Q[2])%x%matrix(1,npc[2],1)
  > Mu<-20*matrix(runif(Q[1]*Q[2]),Q[1],Q[2])
  > Y1 <- matrix(runif(n[1]*n[2]),n[1],n[2])
  > Y2 <- matrix(runif(n[1]*n[2]),n[1],n[2])
  > M<-matrix(rnorm(n[1]*n[2],sd=5),n[1],n[2])+Z1%*%Mu%*%t(Z2)+4.2*Y1-1.6*Y2 ## adjacency matrix
  > 
  > ## estimation
  > my_model <- BM_gaussian_covariates("LBM",M,list(Y1,Y2) , plotting='', explore_min=2, explore_max=2, ncores=2, verbosity=0)
  > my_model$estimate()
  
  > which.max(my_model$ICL)
  [1] 2
  > 
  > proc.time()
     user  system elapsed 
     0.70    0.14    0.82 
Running the tests in 'tests/BM_gaussian_multivariate_independent_homoscedastic.R' failed.
Complete output:
  > require('blockmodels')
  Loading required package: blockmodels
  > set.seed(12)
  > ##
  > ## SBM
  > ##
  > 
  > ## generation of one SBM network
  > npc <- 10 # nodes per class
  > Q <- 2 # classes
  > n <- npc * Q # nodes
  > Z<-diag(Q)%x%matrix(1,npc,1)
  > Mu1<-4*matrix(runif(Q*Q),Q,Q)
  > Mu2<-4*matrix(runif(Q*Q),Q,Q)
  > M1<-matrix(rnorm(n*n,sd=5),n,n)+Z%*%Mu1%*%t(Z) ## adjacency
  > M2<-matrix(rnorm(n*n,sd=5),n,n)+Z%*%Mu2%*%t(Z) ## adjacency
  > 
  > ## estimation
  > my_model <- BM_gaussian_multivariate_independent_homoscedastic("SBM",list(M1,M2) , plotting='', explore_min=2, explore_max=2, ncores=2, verbosity=0)
  > my_model$estimate()
  
  
  > which.max(my_model$ICL)
  [1] 2
  > 
  > ##
  > ## SBM symmetric
  > ##
  > 
  > ## generation of one SBM_sym network
  > npc <- 10 # nodes per class
  > Q <- 2 # classes
  > n <- npc * Q # nodes
  > Z<-diag(Q)%x%matrix(1,npc,1)
  > Mu1<-4*matrix(runif(Q*Q),Q,Q)
  > Mu2<-4*matrix(runif(Q*Q),Q,Q)
  > Mu1[lower.tri(Mu1)]<-t(Mu1)[lower.tri(Mu1)]
  > Mu2[lower.tri(Mu2)]<-t(Mu2)[lower.tri(Mu2)]
  > M1<-matrix(rnorm(n*n,sd=5),n,n)+Z%*%Mu1%*%t(Z) ## adjacency
  > M2<-matrix(rnorm(n*n,sd=5),n,n)+Z%*%Mu2%*%t(Z) ## adjacency
  > M1[lower.tri(M1)]<-t(M1)[lower.tri(M1)]
  > M2[lower.tri(M2)]<-t(M2)[lower.tri(M2)]
  > 
  > ## estimation
  > my_model <- BM_gaussian_multivariate_independent_homoscedastic("SBM_sym",list(M1,M2) , plotting='', explore_min=2, explore_max=2, ncores=2, verbosity=0)
  > my_model$estimate()
  
  
  > which.max(my_model$ICL)
  [1] 1
  > 
  > ##
  > ## LBM
  > ##
  > 
  > ## generation of one LBM network
  > npc <- c(20,10) # nodes per class
  > Q <- c(1,2) # classes
  > n <- npc * Q # nodes
  > Z1<-diag(Q[1])%x%matrix(1,npc[1],1)
  > Z2<-diag(Q[2])%x%matrix(1,npc[2],1)
  > Mu1<-4*matrix(runif(Q[1]*Q[2]),Q[1],Q[2])
  > Mu2<-4*matrix(runif(Q[1]*Q[2]),Q[1],Q[2])
  > M1<-matrix(rnorm(n[1]*n[2],sd=5),n[1],n[2])+Z1%*%Mu1%*%t(Z2) ## adjacency
  > M2<-matrix(rnorm(n[1]*n[2],sd=5),n[1],n[2])+Z1%*%Mu2%*%t(Z2) ## adjacency
  > 
  > ## estimation
  > my_model <- BM_gaussian_multivariate_independent_homoscedastic("LBM",list(M1,M2) , plotting='', explore_min=2, explore_max=2, ncores=2, verbosity=0)
  > my_model$estimate()
  
  > which.max(my_model$ICL)
  [1] 2
  > 
  > proc.time()
     user  system elapsed 
     0.67    0.06    0.73