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diff --git a/pkg/R/constructionModelesLassoMLE.R b/pkg/R/constructionModelesLassoMLE.R
deleted file mode 100644
index ba6f125..0000000
--- a/pkg/R/constructionModelesLassoMLE.R
+++ /dev/null
@@ -1,91 +0,0 @@
-#' constructionModelesLassoMLE
-#'
-#' Construct a collection of models with the Lasso-MLE procedure.
-#' 
-#' @param phiInit an initialization for phi, get by initSmallEM.R
-#' @param rhoInit an initialization for rho, get by initSmallEM.R
-#' @param piInit an initialization for pi, get by initSmallEM.R
-#' @param gamInit an initialization for gam, get by initSmallEM.R
-#' @param mini integer, minimum number of iterations in the EM algorithm, by default = 10
-#' @param maxi integer, maximum number of iterations in the EM algorithm, by default = 100
-#' @param gamma integer for the power in the penaly, by default = 1
-#' @param X matrix of covariates (of size n*p)
-#' @param Y matrix of responses (of size n*m)
-#' @param eps real, threshold to say the EM algorithm converges, by default = 1e-4
-#' @param S output of selectVariables.R
-#' @param ncores Number of cores, by default = 3
-#' @param fast TRUE to use compiled C code, FALSE for R code only
-#' @param verbose TRUE to show some execution traces
-#' 
-#' @return a list with several models, defined by phi, rho, pi, llh
-#'
-#' @export
-constructionModelesLassoMLE = function( phiInit, rhoInit, piInit, gamInit, mini, maxi,gamma, X, Y,
-	 eps, S, ncores=3, fast=TRUE, verbose=FALSE)
-{
-	if (ncores > 1)
-	{
-		cl = parallel::makeCluster(ncores, outfile='')
-		parallel::clusterExport( cl, envir=environment(),
-			varlist=c("phiInit","rhoInit","gamInit","mini","maxi","gamma","X","Y","eps",
-			"S","ncores","fast","verbose") )
-	}
-
-	# Individual model computation
-	computeAtLambda <- function(lambda)
-	{
-		if (ncores > 1)
-			require("valse") #nodes start with an empty environment
-
-		if (verbose)
-			print(paste("Computations for lambda=",lambda))
-
-		n = dim(X)[1]
-		p = dim(phiInit)[1]
-		m = dim(phiInit)[2]
-		k = dim(phiInit)[3]
-		sel.lambda = S[[lambda]]$selected
-#		col.sel = which(colSums(sel.lambda)!=0) #if boolean matrix
-		col.sel <- which( sapply(sel.lambda,length) > 0 ) #if list of selected vars
-		if (length(col.sel) == 0)
-			return (NULL)
-
-		# lambda == 0 because we compute the EMV: no penalization here
-		res = EMGLLF(phiInit[col.sel,,],rhoInit,piInit,gamInit,mini,maxi,gamma,0,
-			X[,col.sel], Y, eps, fast)
-		
-		# Eval dimension from the result + selected
-		phiLambda2 = res$phi
-		rhoLambda = res$rho
-		piLambda = res$pi
-		phiLambda = array(0, dim = c(p,m,k))
-		for (j in seq_along(col.sel))
-			phiLambda[col.sel[j],sel.lambda[[j]],] = phiLambda2[j,sel.lambda[[j]],]
-		dimension = length(unlist(sel.lambda))
-
-		# Computation of the loglikelihood
-		densite = vector("double",n)
-		for (r in 1:k)
-		{
-		  if (length(col.sel)==1){
-		    delta = (Y%*%rhoLambda[,,r] - (X[, col.sel]%*%t(phiLambda[col.sel,,r])))
-		  } else delta = (Y%*%rhoLambda[,,r] - (X[, col.sel]%*%phiLambda[col.sel,,r]))
-			densite = densite + piLambda[r] *
-				det(rhoLambda[,,r])/(sqrt(2*base::pi))^m * exp(-diag(tcrossprod(delta))/2.0)
-		}
-		llhLambda = c( sum(log(densite)), (dimension+m+1)*k-1 )
-		list("phi"= phiLambda, "rho"= rhoLambda, "pi"= piLambda, "llh" = llhLambda)
-	}
-
-	# For each lambda, computation of the parameters
-	out =
-		if (ncores > 1)
-			parLapply(cl, 1:length(S), computeAtLambda)
-		else
-			lapply(1:length(S), computeAtLambda)
-
-	if (ncores > 1)
-		parallel::stopCluster(cl)
-
-	out
-}