ite = ite+1
   }
-  
+
   affec = apply(gam, 1, which.max)
   return(list("phi"=phi, "rho"=rho, "pi"=pi, "LLF"=LLF, "S"=S, "affec" = affec ))
 }
 
 {
        if (ncores > 1)
        {
-               cl = parallel::makeCluster(ncores)
+               cl = parallel::makeCluster(ncores, outfile='')
                parallel::clusterExport( cl, envir=environment(),
                        varlist=c("phiInit","rhoInit","gamInit","mini","maxi","gamma","X","Y","thresh",
                        "tau","S","ncores","verbose") )
        out =
                if (ncores > 1)
                        parLapply(cl, 1:length(S), computeAtLambda)
-       else
-               lapply(1:length(S), computeAtLambda)
+               else
+                       lapply(1:length(S), computeAtLambda)
 
        if (ncores > 1)
                parallel::stopCluster(cl)
 
 
   if (ncores > 1)
        {
-    cl = parallel::makeCluster(ncores)
+    cl = parallel::makeCluster(ncores, outfile='')
     parallel::clusterExport( cl, envir=environment(),
                        varlist=c("A1","Size","Pi","Rho","mini","maxi","X","Y","tau",
                        "Rank","m","phi","ncores","verbose") )
 
                #Pour un groupe de modeles (même k, différents lambda):
                llh = matrix(ncol = 2)
                for (l in seq_along(models))
-                       llh = rbind(llh, models[[l]]$llh)
+                       llh = rbind(llh, models[[l]]$llh) #TODO: LLF? harmonize between EMGLLF and EMGrank?
                LLH = llh[-1,1]
                D = llh[-1,2]
                k = length(models[[1]]$pi)
        tableauRecap = tableauRecap[rowSums(tableauRecap[, 2:4])!=0,]
   tableauRecap = tableauRecap[(tableauRecap[,1])!=Inf,]
   data = cbind(1:dim(tableauRecap)[1], tableauRecap[,2], tableauRecap[,2], tableauRecap[,1])
-
+browser()
   modSel = capushe::capushe(data, n)
   indModSel <-
                if (selecMod == 'DDSE')
 
 {
        if (ncores > 1)
        {
-               cl = parallel::makeCluster(ncores)
+               cl = parallel::makeCluster(ncores, outfile='')
                parallel::clusterExport(cl=cl,
                        varlist=c("phiInit","rhoInit","gamInit","mini","maxi","glambda","X","Y","thresh","tau"),
                        envir=environment())