From: Benjamin Auder <benjamin.auder@somewhere>
Date: Fri, 14 Apr 2017 15:46:32 +0000 (+0200)
Subject: indent everything: google rules...
X-Git-Url: https://git.auder.net/doc/html/css/current/scripts/R.css?a=commitdiff_plain;h=ffdf94474d96cdd3e9d304ce809df7e62aa957ed;p=valse.git

indent everything: google rules...
---

diff --git a/pkg/R/EMGLLF.R b/pkg/R/EMGLLF.R
index 92351d7..5ef231e 100644
--- a/pkg/R/EMGLLF.R
+++ b/pkg/R/EMGLLF.R
@@ -1,4 +1,4 @@
-#' EMGLLF
+#' EMGLLF 
 #'
 #' Description de EMGLLF
 #'
@@ -22,122 +22,118 @@
 #'   S : ... affec : ...
 #'
 #' @export
-EMGLLF <- function(phiInit, rhoInit, piInit, gamInit,
-                   mini, maxi, gamma, lambda, X, Y, eps, fast=TRUE)
-{
+EMGLLF <- function(phiInit, rhoInit, piInit, gamInit, mini, maxi, gamma, lambda, 
+  X, Y, eps, fast = TRUE)
+  {
   if (!fast)
   {
     # Function in R
-    return (.EMGLLF_R(phiInit,rhoInit,piInit,gamInit,mini,maxi,gamma,lambda,X,Y,eps))
+    return(.EMGLLF_R(phiInit, rhoInit, piInit, gamInit, mini, maxi, gamma, lambda, 
+      X, Y, eps))
   }
   
   # Function in C
-  n = nrow(X) #nombre d'echantillons
-  p = ncol(X) #nombre de covariables
-  m = ncol(Y) #taille de Y (multivarié)
-  k = length(piInit) #nombre de composantes dans le mélange
-  .Call("EMGLLF",
-        phiInit, rhoInit, piInit, gamInit, mini, maxi, gamma, lambda, X, Y, eps,
-        phi=double(p*m*k), rho=double(m*m*k), pi=double(k), LLF=double(maxi),
-        S=double(p*m*k), affec=integer(n),
-        n, p, m, k,
-        PACKAGE="valse")
+  n <- nrow(X)  #nombre d'echantillons
+  p <- ncol(X)  #nombre de covariables
+  m <- ncol(Y)  #taille de Y (multivarié)
+  k <- length(piInit)  #nombre de composantes dans le mélange
+  .Call("EMGLLF", phiInit, rhoInit, piInit, gamInit, mini, maxi, gamma, lambda, 
+    X, Y, eps, phi = double(p * m * k), rho = double(m * m * k), pi = double(k), 
+    LLF = double(maxi), S = double(p * m * k), affec = integer(n), n, p, m, k, 
+    PACKAGE = "valse")
 }
 
 # R version - slow but easy to read
-.EMGLLF_R = function(phiInit,rhoInit,piInit,gamInit,mini,maxi,gamma,lambda,X2,Y,eps)
-{
+.EMGLLF_R <- function(phiInit, rhoInit, piInit, gamInit, mini, maxi, gamma, lambda, 
+  X2, Y, eps)
+  {
   # Matrix dimensions
-  n = dim(Y)[1]
-  if (length(dim(phiInit)) == 2){
-    p = 1
-    m = dim(phiInit)[1]
-    k = dim(phiInit)[2]
-  } else { 
-    p = dim(phiInit)[1]
-    m = dim(phiInit)[2]
-    k = dim(phiInit)[3]
+  n <- dim(Y)[1]
+  if (length(dim(phiInit)) == 2)
+  {
+    p <- 1
+    m <- dim(phiInit)[1]
+    k <- dim(phiInit)[2]
+  } else
+  {
+    p <- dim(phiInit)[1]
+    m <- dim(phiInit)[2]
+    k <- dim(phiInit)[3]
   }
-  X = matrix(nrow = n, ncol = p)
-  X[1:n,1:p] = X2
+  X <- matrix(nrow = n, ncol = p)
+  X[1:n, 1:p] <- X2
   # Outputs
-  phi = array(NA, dim = c(p,m,k))
-  phi[1:p,,] = phiInit
-  rho = rhoInit
-  pi = piInit
-  llh = -Inf
-  S = array(0, dim=c(p,m,k))
+  phi <- array(NA, dim = c(p, m, k))
+  phi[1:p, , ] <- phiInit
+  rho <- rhoInit
+  pi <- piInit
+  llh <- -Inf
+  S <- array(0, dim = c(p, m, k))
   
   # Algorithm variables
-  gam = gamInit
-  Gram2 = array(0, dim=c(p,p,k))
-  ps2 = array(0, dim=c(p,m,k))
-  X2 = array(0, dim=c(n,p,k))
-  Y2 = array(0, dim=c(n,m,k))
-  EPS = 1e-15
+  gam <- gamInit
+  Gram2 <- array(0, dim = c(p, p, k))
+  ps2 <- array(0, dim = c(p, m, k))
+  X2 <- array(0, dim = c(n, p, k))
+  Y2 <- array(0, dim = c(n, m, k))
+  EPS <- 1e-15
   
   for (ite in 1:maxi)
   {
     # Remember last pi,rho,phi values for exit condition in the end of loop
-    Phi = phi
-    Rho = rho
-    Pi = pi
+    Phi <- phi
+    Rho <- rho
+    Pi <- pi
     
     # Computations associated to X and Y
     for (r in 1:k)
     {
-      for (mm in 1:m)
-        Y2[,mm,r] = sqrt(gam[,r]) * Y[,mm]
-      for (i in 1:n)
-        X2[i,,r] = sqrt(gam[i,r]) * X[i,]
-      for (mm in 1:m)
-        ps2[,mm,r] = crossprod(X2[,,r],Y2[,mm,r])
+      for (mm in 1:m) Y2[, mm, r] <- sqrt(gam[, r]) * Y[, mm]
+      for (i in 1:n) X2[i, , r] <- sqrt(gam[i, r]) * X[i, ]
+      for (mm in 1:m) ps2[, mm, r] <- crossprod(X2[, , r], Y2[, mm, r])
       for (j in 1:p)
       {
-        for (s in 1:p)
-          Gram2[j,s,r] = crossprod(X2[,j,r], X2[,s,r])
+        for (s in 1:p) Gram2[j, s, r] <- crossprod(X2[, j, r], X2[, s, r])
       }
     }
     
-    #########
-    #M step #
-    #########
+    ######### M step #
     
     # For pi
-    b = sapply( 1:k, function(r) sum(abs(phi[,,r])) )
-    gam2 = colSums(gam)
-    a = sum(gam %*% log(pi))
+    b <- sapply(1:k, function(r) sum(abs(phi[, , r])))
+    gam2 <- colSums(gam)
+    a <- sum(gam %*% log(pi))
     
     # While the proportions are nonpositive
-    kk = 0
-    pi2AllPositive = FALSE
+    kk <- 0
+    pi2AllPositive <- FALSE
     while (!pi2AllPositive)
     {
-      pi2 = pi + 0.1^kk * ((1/n)*gam2 - pi)
-      pi2AllPositive = all(pi2 >= 0)
-      kk = kk+1
+      pi2 <- pi + 0.1^kk * ((1/n) * gam2 - pi)
+      pi2AllPositive <- all(pi2 >= 0)
+      kk <- kk + 1
     }
     
     # t(m) is the largest value in the grid O.1^k such that it is nonincreasing
-    while( kk < 1000 && -a/n + lambda * sum(pi^gamma * b) <
-           -sum(gam2 * log(pi2))/n + lambda * sum(pi2^gamma * b) )
-    {
-      pi2 = pi + 0.1^kk * (1/n*gam2 - pi)
-      kk = kk + 1
+    while (kk < 1000 && -a/n + lambda * sum(pi^gamma * b) < -sum(gam2 * log(pi2))/n + 
+      lambda * sum(pi2^gamma * b))
+      {
+      pi2 <- pi + 0.1^kk * (1/n * gam2 - pi)
+      kk <- kk + 1
     }
-    t = 0.1^kk
-    pi = (pi + t*(pi2-pi)) / sum(pi + t*(pi2-pi))
+    t <- 0.1^kk
+    pi <- (pi + t * (pi2 - pi))/sum(pi + t * (pi2 - pi))
     
-    #For phi and rho
+    # For phi and rho
     for (r in 1:k)
     {
       for (mm in 1:m)
       {
-        ps = 0
-        for (i in 1:n)
-          ps = ps + Y2[i,mm,r] * sum(X2[i,,r] * phi[,mm,r])
-        nY2 = sum(Y2[,mm,r]^2)
-        rho[mm,mm,r] = (ps+sqrt(ps^2+4*nY2*gam2[r])) / (2*nY2)
+        ps <- 0
+        for (i in 1:n) ps <- ps + Y2[i, mm, r] * sum(X2[i, , r] * phi[, mm, 
+          r])
+        nY2 <- sum(Y2[, mm, r]^2)
+        rho[mm, mm, r] <- (ps + sqrt(ps^2 + 4 * nY2 * gam2[r]))/(2 * nY2)
       }
     }
     
@@ -147,46 +143,45 @@ EMGLLF <- function(phiInit, rhoInit, piInit, gamInit,
       {
         for (mm in 1:m)
         {
-          S[j,mm,r] = -rho[mm,mm,r]*ps2[j,mm,r] + sum(phi[-j,mm,r] * Gram2[j,-j,r])
-          if (abs(S[j,mm,r]) <= n*lambda*(pi[r]^gamma))
-            phi[j,mm,r]=0
-          else if(S[j,mm,r] > n*lambda*(pi[r]^gamma))
-            phi[j,mm,r] = (n*lambda*(pi[r]^gamma)-S[j,mm,r]) / Gram2[j,j,r]
-          else
-            phi[j,mm,r] = -(n*lambda*(pi[r]^gamma)+S[j,mm,r]) / Gram2[j,j,r]
+          S[j, mm, r] <- -rho[mm, mm, r] * ps2[j, mm, r] + sum(phi[-j, mm, 
+          r] * Gram2[j, -j, r])
+          if (abs(S[j, mm, r]) <= n * lambda * (pi[r]^gamma)) 
+          phi[j, mm, r] <- 0 else if (S[j, mm, r] > n * lambda * (pi[r]^gamma)) 
+          phi[j, mm, r] <- (n * lambda * (pi[r]^gamma) - S[j, mm, r])/Gram2[j, 
+            j, r] else phi[j, mm, r] <- -(n * lambda * (pi[r]^gamma) + S[j, mm, r])/Gram2[j, 
+          j, r]
         }
       }
     }
     
-    ########
-    #E step#
-    ########
+    ######## E step#
     
     # Precompute det(rho[,,r]) for r in 1...k
-    detRho = sapply(1:k, function(r) det(rho[,,r]))
-    gam1 = matrix(0, nrow = n, ncol = k)
+    detRho <- sapply(1:k, function(r) det(rho[, , r]))
+    gam1 <- matrix(0, nrow = n, ncol = k)
     for (i in 1:n)
     {
       # Update gam[,]
       for (r in 1:k)
       {
-        gam1[i,r] = pi[r]*exp(-0.5*sum((Y[i,]%*%rho[,,r]-X[i,]%*%phi[,,r])^2))*detRho[r]
+        gam1[i, r] <- pi[r] * exp(-0.5 * sum((Y[i, ] %*% rho[, , r] - X[i, 
+          ] %*% phi[, , r])^2)) * detRho[r]
       }
     }
-    gam = gam1 / rowSums(gam1)
-    sumLogLLH = sum(log(rowSums(gam)) - log((2*base::pi)^(m/2)))
-    sumPen = sum(pi^gamma * b)
-    last_llh = llh
-    llh = -sumLogLLH/n + lambda*sumPen
-    dist = ifelse( ite == 1, llh, (llh-last_llh) / (1+abs(llh)) )
-    Dist1 = max( (abs(phi-Phi)) / (1+abs(phi)) )
-    Dist2 = max( (abs(rho-Rho)) / (1+abs(rho)) )
-    Dist3 = max( (abs(pi-Pi)) / (1+abs(Pi)) )
-    dist2 = max(Dist1,Dist2,Dist3)
+    gam <- gam1/rowSums(gam1)
+    sumLogLLH <- sum(log(rowSums(gam)) - log((2 * base::pi)^(m/2)))
+    sumPen <- sum(pi^gamma * b)
+    last_llh <- llh
+    llh <- -sumLogLLH/n + lambda * sumPen
+    dist <- ifelse(ite == 1, llh, (llh - last_llh)/(1 + abs(llh)))
+    Dist1 <- max((abs(phi - Phi))/(1 + abs(phi)))
+    Dist2 <- max((abs(rho - Rho))/(1 + abs(rho)))
+    Dist3 <- max((abs(pi - Pi))/(1 + abs(Pi)))
+    dist2 <- max(Dist1, Dist2, Dist3)
     
-    if (ite >= mini && (dist >= eps || dist2 >= sqrt(eps)))
+    if (ite >= mini && (dist >= eps || dist2 >= sqrt(eps))) 
       break
   }
   
-  list( "phi"=phi, "rho"=rho, "pi"=pi, "llh"=llh, "S"=S)
+  list(phi = phi, rho = rho, pi = pi, llh = llh, S = S)
 }
diff --git a/pkg/R/EMGrank.R b/pkg/R/EMGrank.R
index 5eea322..436b289 100644
--- a/pkg/R/EMGrank.R
+++ b/pkg/R/EMGrank.R
@@ -1,4 +1,4 @@
-#' EMGrank
+#' EMGrank 
 #'
 #' Description de EMGrank
 #'
@@ -16,108 +16,106 @@
 #'   LLF : log vraisemblance associé à cet échantillon, pour les valeurs estimées des paramètres
 #'
 #' @export
-EMGrank <- function(Pi, Rho, mini, maxi, X, Y, tau, rank, fast=TRUE)
+EMGrank <- function(Pi, Rho, mini, maxi, X, Y, tau, rank, fast = TRUE)
 {
-	if (!fast)
-	{
-		# Function in R
-		return (.EMGrank_R(Pi, Rho, mini, maxi, X, Y, tau, rank))
-	}
-
-	# Function in C
-	n = nrow(X) #nombre d'echantillons
-	p = ncol(X) #nombre de covariables
-	m = ncol(Y) #taille de Y (multivarié)
-	k = length(Pi) #nombre de composantes dans le mélange
-	.Call("EMGrank",
-		Pi, Rho, mini, maxi, X, Y, tau, rank,
-		phi=double(p*m*k), LLF=double(1),
-		n, p, m, k,
-		PACKAGE="valse")
+  if (!fast)
+  {
+    # Function in R
+    return(.EMGrank_R(Pi, Rho, mini, maxi, X, Y, tau, rank))
+  }
+  
+  # Function in C
+  n <- nrow(X)  #nombre d'echantillons
+  p <- ncol(X)  #nombre de covariables
+  m <- ncol(Y)  #taille de Y (multivarié)
+  k <- length(Pi)  #nombre de composantes dans le mélange
+  .Call("EMGrank", Pi, Rho, mini, maxi, X, Y, tau, rank, phi = double(p * m * k), 
+    LLF = double(1), n, p, m, k, PACKAGE = "valse")
 }
 
-#helper to always have matrices as arg (TODO: put this elsewhere? improve?)
-# --> Yes, we should use by-columns storage everywhere... [later!]
+# helper to always have matrices as arg (TODO: put this elsewhere? improve?)  -->
+# Yes, we should use by-columns storage everywhere... [later!]
 matricize <- function(X)
 {
-	if (!is.matrix(X))
-		return (t(as.matrix(X)))
-	return (X)
+  if (!is.matrix(X)) 
+    return(t(as.matrix(X)))
+  return(X)
 }
 
 # R version - slow but easy to read
-.EMGrank_R = function(Pi, Rho, mini, maxi, X, Y, tau, rank)
+.EMGrank_R <- function(Pi, Rho, mini, maxi, X, Y, tau, rank)
 {
-  #matrix dimensions
-  n = dim(X)[1]
-  p = dim(X)[2]
-  m = dim(Rho)[2]
-  k = dim(Rho)[3]
-
-  #init outputs
-  phi = array(0, dim=c(p,m,k))
-  Z = rep(1, n)
-  LLF = 0
-
-  #local variables
-  Phi = array(0, dim=c(p,m,k))
-  deltaPhi = c()
-  sumDeltaPhi = 0.
-  deltaPhiBufferSize = 20
-
-  #main loop
-  ite = 1
-  while (ite<=mini || (ite<=maxi && sumDeltaPhi>tau))
-	{
-    #M step: update for Beta ( and then phi)
-    for(r in 1:k)
-		{
-      Z_indice = seq_len(n)[Z==r] #indices where Z == r
-      if (length(Z_indice) == 0)
+  # matrix dimensions
+  n <- dim(X)[1]
+  p <- dim(X)[2]
+  m <- dim(Rho)[2]
+  k <- dim(Rho)[3]
+  
+  # init outputs
+  phi <- array(0, dim = c(p, m, k))
+  Z <- rep(1, n)
+  LLF <- 0
+  
+  # local variables
+  Phi <- array(0, dim = c(p, m, k))
+  deltaPhi <- c()
+  sumDeltaPhi <- 0
+  deltaPhiBufferSize <- 20
+  
+  # main loop
+  ite <- 1
+  while (ite <= mini || (ite <= maxi && sumDeltaPhi > tau))
+  {
+    # M step: update for Beta ( and then phi)
+    for (r in 1:k)
+    {
+      Z_indice <- seq_len(n)[Z == r]  #indices where Z == r
+      if (length(Z_indice) == 0) 
         next
-      #U,S,V = SVD of (t(Xr)Xr)^{-1} * t(Xr) * Yr
-      s = svd( MASS::ginv(crossprod(matricize(X[Z_indice,]))) %*%
-				crossprod(matricize(X[Z_indice,]),matricize(Y[Z_indice,])) )
-      S = s$d
-      #Set m-rank(r) singular values to zero, and recompose
-      #best rank(r) approximation of the initial product
-      if(rank[r] < length(S))
-        S[(rank[r]+1):length(S)] = 0
-      phi[,,r] = s$u %*% diag(S) %*% t(s$v) %*% Rho[,,r]
+      # U,S,V = SVD of (t(Xr)Xr)^{-1} * t(Xr) * Yr
+      s <- svd(MASS::ginv(crossprod(matricize(X[Z_indice, ]))) %*% crossprod(matricize(X[Z_indice, 
+        ]), matricize(Y[Z_indice, ])))
+      S <- s$d
+      # Set m-rank(r) singular values to zero, and recompose best rank(r) approximation
+      # of the initial product
+      if (rank[r] < length(S)) 
+        S[(rank[r] + 1):length(S)] <- 0
+      phi[, , r] <- s$u %*% diag(S) %*% t(s$v) %*% Rho[, , r]
     }
-
-		#Step E and computation of the loglikelihood
-		sumLogLLF2 = 0
-		for(i in seq_len(n))
-		{
-			sumLLF1 = 0
-			maxLogGamIR = -Inf
-			for (r in seq_len(k))
-			{
-				dotProduct = tcrossprod(Y[i,]%*%Rho[,,r]-X[i,]%*%phi[,,r])
-				logGamIR = log(Pi[r]) + log(det(Rho[,,r])) - 0.5*dotProduct
-				#Z[i] = index of max (gam[i,])
-				if(logGamIR > maxLogGamIR)
-				{
-					Z[i] = r
-					maxLogGamIR = logGamIR
-				}
-				sumLLF1 = sumLLF1 + exp(logGamIR) / (2*pi)^(m/2)
-			}
-			sumLogLLF2 = sumLogLLF2 + log(sumLLF1)
-		}
-
-		LLF = -1/n * sumLogLLF2
-
-		#update distance parameter to check algorithm convergence (delta(phi, Phi))
-		deltaPhi = c( deltaPhi, max( (abs(phi-Phi)) / (1+abs(phi)) ) ) #TODO: explain?
-		if (length(deltaPhi) > deltaPhiBufferSize)
-		  deltaPhi = deltaPhi[2:length(deltaPhi)]
-		sumDeltaPhi = sum(abs(deltaPhi))
-
-		#update other local variables
-		Phi = phi
-		ite = ite+1
+    
+    # Step E and computation of the loglikelihood
+    sumLogLLF2 <- 0
+    for (i in seq_len(n))
+    {
+      sumLLF1 <- 0
+      maxLogGamIR <- -Inf
+      for (r in seq_len(k))
+      {
+        dotProduct <- tcrossprod(Y[i, ] %*% Rho[, , r] - X[i, ] %*% phi[, 
+          , r])
+        logGamIR <- log(Pi[r]) + log(det(Rho[, , r])) - 0.5 * dotProduct
+        # Z[i] = index of max (gam[i,])
+        if (logGamIR > maxLogGamIR)
+        {
+          Z[i] <- r
+          maxLogGamIR <- logGamIR
+        }
+        sumLLF1 <- sumLLF1 + exp(logGamIR)/(2 * pi)^(m/2)
+      }
+      sumLogLLF2 <- sumLogLLF2 + log(sumLLF1)
+    }
+    
+    LLF <- -1/n * sumLogLLF2
+    
+    # update distance parameter to check algorithm convergence (delta(phi, Phi))
+    deltaPhi <- c(deltaPhi, max((abs(phi - Phi))/(1 + abs(phi))))  #TODO: explain?
+    if (length(deltaPhi) > deltaPhiBufferSize) 
+      deltaPhi <- deltaPhi[2:length(deltaPhi)]
+    sumDeltaPhi <- sum(abs(deltaPhi))
+    
+    # update other local variables
+    Phi <- phi
+    ite <- ite + 1
   }
-  return(list("phi"=phi, "LLF"=LLF))
+  return(list(phi = phi, LLF = LLF))
 }
diff --git a/pkg/R/computeGridLambda.R b/pkg/R/computeGridLambda.R
index b295535..4b68bcd 100644
--- a/pkg/R/computeGridLambda.R
+++ b/pkg/R/computeGridLambda.R
@@ -1,10 +1,10 @@
-#' computeGridLambda
+#' computeGridLambda 
 #'
 #' Construct the data-driven grid for the regularization parameters used for the Lasso estimator
 #'
 #' @param phiInit value for phi
-#' @param rhoInit	value for rho
-#' @param piInit	value for pi
+#' @param rhoInit\tvalue for rho
+#' @param piInit\tvalue for pi
 #' @param gamInit value for gamma
 #' @param X matrix of covariates (of size n*p)
 #' @param Y matrix of responses (of size n*m)
@@ -16,21 +16,20 @@
 #' @return the grid of regularization parameters
 #'
 #' @export
-computeGridLambda = function(phiInit, rhoInit, piInit, gamInit, X, Y,
-	gamma, mini, maxi, tau, fast=TRUE)
-{
-	n = nrow(X)
-	p = dim(phiInit)[1]
-	m = dim(phiInit)[2]
-	k = dim(phiInit)[3]
-
-	list_EMG = EMGLLF(phiInit, rhoInit, piInit, gamInit, mini, maxi,
-		gamma, lambda=0, X, Y, tau, fast)
-	grid = array(0, dim=c(p,m,k))
-	for (i in 1:p)
-	{
-		for (j in 1:m)
-			grid[i,j,] = abs(list_EMG$S[i,j,]) / (n*list_EMG$pi^gamma)
-	}
-	sort( unique(grid) )
+computeGridLambda <- function(phiInit, rhoInit, piInit, gamInit, X, Y, gamma, mini, 
+  maxi, tau, fast = TRUE)
+  {
+  n <- nrow(X)
+  p <- dim(phiInit)[1]
+  m <- dim(phiInit)[2]
+  k <- dim(phiInit)[3]
+  
+  list_EMG <- EMGLLF(phiInit, rhoInit, piInit, gamInit, mini, maxi, gamma, lambda = 0, 
+    X, Y, tau, fast)
+  grid <- array(0, dim = c(p, m, k))
+  for (i in 1:p)
+  {
+    for (j in 1:m) grid[i, j, ] <- abs(list_EMG$S[i, j, ])/(n * list_EMG$pi^gamma)
+  }
+  sort(unique(grid))
 }
diff --git a/pkg/R/constructionModelesLassoMLE.R b/pkg/R/constructionModelesLassoMLE.R
index ba6f125..760da40 100644
--- a/pkg/R/constructionModelesLassoMLE.R
+++ b/pkg/R/constructionModelesLassoMLE.R
@@ -1,4 +1,4 @@
-#' constructionModelesLassoMLE
+#' constructionModelesLassoMLE 
 #'
 #' Construct a collection of models with the Lasso-MLE procedure.
 #' 
@@ -20,72 +20,72 @@
 #' @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
+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)
+    }
+    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
 }
diff --git a/pkg/R/generateXY.R b/pkg/R/generateXY.R
index 069c470..fe86045 100644
--- a/pkg/R/generateXY.R
+++ b/pkg/R/generateXY.R
@@ -1,4 +1,4 @@
-#' generateXY
+#' generateXY 
 #'
 #' Generate a sample of (X,Y) of size n
 #'
@@ -12,28 +12,28 @@
 #' @return list with X and Y
 #'
 #' @export
-generateXY = function(n, π, meanX, β, covX, covY)
+generateXY <- function(n, π, meanX, β, covX, covY)
 {
-	p <- dim(covX)[1]
-	m <- dim(covY)[1]
-	k <- dim(covY)[3]
-
-	X <- matrix(nrow=0, ncol=p)
-	Y <- matrix(nrow=0, ncol=m)
-
-	#random generation of the size of each population in X~Y (unordered)
-	sizePop <- rmultinom(1, n, π)
-	class <- c() #map i in 1:n --> index of class in 1:k
-
-	for (i in 1:k)
-	{
-		class <- c(class, rep(i, sizePop[i]))
-		newBlockX <- MASS::mvrnorm(sizePop[i], meanX, covX)
-		X <- rbind( X, newBlockX )
-		Y <- rbind( Y, t(apply( newBlockX, 1, function(row)
-			MASS::mvrnorm(1, row %*% β[,,i], covY[,,i]) )) )
-	}
-
-	shuffle = sample(n)
-	list("X"=X[shuffle,], "Y"=Y[shuffle,], "class"=class[shuffle])
+  p <- dim(covX)[1]
+  m <- dim(covY)[1]
+  k <- dim(covY)[3]
+  
+  X <- matrix(nrow = 0, ncol = p)
+  Y <- matrix(nrow = 0, ncol = m)
+  
+  # random generation of the size of each population in X~Y (unordered)
+  sizePop <- rmultinom(1, n, π)
+  class <- c()  #map i in 1:n --> index of class in 1:k
+  
+  for (i in 1:k)
+  {
+    class <- c(class, rep(i, sizePop[i]))
+    newBlockX <- MASS::mvrnorm(sizePop[i], meanX, covX)
+    X <- rbind(X, newBlockX)
+    Y <- rbind(Y, t(apply(newBlockX, 1, function(row) MASS::mvrnorm(1, row %*% 
+      β[, , i], covY[, , i]))))
+  }
+  
+  shuffle <- sample(n)
+  list(X = X[shuffle, ], Y = Y[shuffle, ], class = class[shuffle])
 }
diff --git a/pkg/R/initSmallEM.R b/pkg/R/initSmallEM.R
index 7ffbade..fafa2c4 100644
--- a/pkg/R/initSmallEM.R
+++ b/pkg/R/initSmallEM.R
@@ -1,4 +1,4 @@
-#' initialization of the EM algorithm
+#' initialization of the EM algorithm 
 #'
 #' @param k number of components
 #' @param X matrix of covariates (of size n*p)
@@ -8,70 +8,75 @@
 #' @export
 #' @importFrom methods new
 #' @importFrom stats cutree dist hclust runif
-initSmallEM = function(k,X,Y, fast=TRUE)
+initSmallEM <- function(k, X, Y, fast = TRUE)
 {
-	n = nrow(Y)
-	m = ncol(Y)
-	p = ncol(X)
-	nIte = 20
-	Zinit1 = array(0, dim=c(n,nIte))
-	betaInit1 = array(0, dim=c(p,m,k,nIte))
-	sigmaInit1 = array(0, dim = c(m,m,k,nIte))
-	phiInit1 = array(0, dim = c(p,m,k,nIte))
-	rhoInit1 = array(0, dim = c(m,m,k,nIte))
-	Gam = matrix(0, n, k)
-	piInit1 = matrix(0,nIte,k)
-	gamInit1 = array(0, dim=c(n,k,nIte))
-	LLFinit1 = list()
-
-	#require(MASS) #Moore-Penrose generalized inverse of matrix
-	for(repet in 1:nIte)
-	{
-	  distance_clus = dist(cbind(X,Y))
-	  tree_hier = hclust(distance_clus)
-	  Zinit1[,repet] = cutree(tree_hier, k)
-
-		for(r in 1:k)
-		{
-			Z = Zinit1[,repet]
-			Z_indice = seq_len(n)[Z == r] #renvoit les indices où Z==r
-			if (length(Z_indice) == 1) {
-			  betaInit1[,,r,repet] = MASS::ginv(crossprod(t(X[Z_indice,]))) %*%
-			    crossprod(t(X[Z_indice,]), Y[Z_indice,])
-			} else {
-			betaInit1[,,r,repet] = MASS::ginv(crossprod(X[Z_indice,])) %*%
-				crossprod(X[Z_indice,], Y[Z_indice,])
-			}
-			sigmaInit1[,,r,repet] = diag(m)
-			phiInit1[,,r,repet] = betaInit1[,,r,repet] #/ sigmaInit1[,,r,repet]
-			rhoInit1[,,r,repet] = solve(sigmaInit1[,,r,repet])
-			piInit1[repet,r] = mean(Z == r)
-		}
-		
-		for(i in 1:n)
-		{
-			for(r in 1:k)
-			{
-				dotProduct = tcrossprod(Y[i,]%*%rhoInit1[,,r,repet]-X[i,]%*%phiInit1[,,r,repet])
-				Gam[i,r] = piInit1[repet,r]*det(rhoInit1[,,r,repet])*exp(-0.5*dotProduct)
-			}
-			sumGamI = sum(Gam[i,])
-			gamInit1[i,,repet]= Gam[i,] / sumGamI
-		}
-		
-		miniInit = 10
-		maxiInit = 11
-		
-		init_EMG = EMGLLF(phiInit1[,,,repet], rhoInit1[,,,repet], piInit1[repet,],
-			gamInit1[,,repet], miniInit, maxiInit, gamma=1, lambda=0, X, Y, eps=1e-4, fast)
-		LLFEessai = init_EMG$LLF
-		LLFinit1[repet] = LLFEessai[length(LLFEessai)]
-	}
-	b = which.min(LLFinit1)
-	phiInit = phiInit1[,,,b]
-	rhoInit = rhoInit1[,,,b]
-	piInit = piInit1[b,]
-	gamInit = gamInit1[,,b]
-
-	return (list(phiInit=phiInit, rhoInit=rhoInit, piInit=piInit, gamInit=gamInit))
+  n <- nrow(Y)
+  m <- ncol(Y)
+  p <- ncol(X)
+  nIte <- 20
+  Zinit1 <- array(0, dim = c(n, nIte))
+  betaInit1 <- array(0, dim = c(p, m, k, nIte))
+  sigmaInit1 <- array(0, dim = c(m, m, k, nIte))
+  phiInit1 <- array(0, dim = c(p, m, k, nIte))
+  rhoInit1 <- array(0, dim = c(m, m, k, nIte))
+  Gam <- matrix(0, n, k)
+  piInit1 <- matrix(0, nIte, k)
+  gamInit1 <- array(0, dim = c(n, k, nIte))
+  LLFinit1 <- list()
+  
+  # require(MASS) #Moore-Penrose generalized inverse of matrix
+  for (repet in 1:nIte)
+  {
+    distance_clus <- dist(cbind(X, Y))
+    tree_hier <- hclust(distance_clus)
+    Zinit1[, repet] <- cutree(tree_hier, k)
+    
+    for (r in 1:k)
+    {
+      Z <- Zinit1[, repet]
+      Z_indice <- seq_len(n)[Z == r]  #renvoit les indices où Z==r
+      if (length(Z_indice) == 1)
+      {
+        betaInit1[, , r, repet] <- MASS::ginv(crossprod(t(X[Z_indice, ]))) %*% 
+          crossprod(t(X[Z_indice, ]), Y[Z_indice, ])
+      } else
+      {
+        betaInit1[, , r, repet] <- MASS::ginv(crossprod(X[Z_indice, ])) %*% 
+          crossprod(X[Z_indice, ], Y[Z_indice, ])
+      }
+      sigmaInit1[, , r, repet] <- diag(m)
+      phiInit1[, , r, repet] <- betaInit1[, , r, repet]  #/ sigmaInit1[,,r,repet]
+      rhoInit1[, , r, repet] <- solve(sigmaInit1[, , r, repet])
+      piInit1[repet, r] <- mean(Z == r)
+    }
+    
+    for (i in 1:n)
+    {
+      for (r in 1:k)
+      {
+        dotProduct <- tcrossprod(Y[i, ] %*% rhoInit1[, , r, repet] - X[i, 
+          ] %*% phiInit1[, , r, repet])
+        Gam[i, r] <- piInit1[repet, r] * det(rhoInit1[, , r, repet]) * exp(-0.5 * 
+          dotProduct)
+      }
+      sumGamI <- sum(Gam[i, ])
+      gamInit1[i, , repet] <- Gam[i, ]/sumGamI
+    }
+    
+    miniInit <- 10
+    maxiInit <- 11
+    
+    init_EMG <- EMGLLF(phiInit1[, , , repet], rhoInit1[, , , repet], piInit1[repet, 
+      ], gamInit1[, , repet], miniInit, maxiInit, gamma = 1, lambda = 0, X, 
+      Y, eps = 1e-04, fast)
+    LLFEessai <- init_EMG$LLF
+    LLFinit1[repet] <- LLFEessai[length(LLFEessai)]
+  }
+  b <- which.min(LLFinit1)
+  phiInit <- phiInit1[, , , b]
+  rhoInit <- rhoInit1[, , , b]
+  piInit <- piInit1[b, ]
+  gamInit <- gamInit1[, , b]
+  
+  return(list(phiInit = phiInit, rhoInit = rhoInit, piInit = piInit, gamInit = gamInit))
 }
diff --git a/pkg/R/main.R b/pkg/R/main.R
index 6b683a5..3b9620d 100644
--- a/pkg/R/main.R
+++ b/pkg/R/main.R
@@ -1,4 +1,4 @@
-#' valse
+#' valse 
 #'
 #' Main function
 #'
@@ -26,134 +26,133 @@
 #' @examples
 #' #TODO: a few examples
 #' @export
-valse = function(X, Y, procedure='LassoMLE', selecMod='DDSE', gamma=1, mini=10, maxi=50,
-                 eps=1e-4, kmin=2, kmax=3, rank.min=1, rank.max=5, ncores_outer=1, ncores_inner=1,
-                 thresh=1e-8,
-                 size_coll_mod=10, fast=TRUE, verbose=FALSE, plot = TRUE)
-{
-  p = dim(X)[2]
-  m = dim(Y)[2]
-  n = dim(X)[1]
+valse <- function(X, Y, procedure = "LassoMLE", selecMod = "DDSE", gamma = 1, mini = 10, 
+  maxi = 50, eps = 1e-04, kmin = 2, kmax = 3, rank.min = 1, rank.max = 5, ncores_outer = 1, 
+  ncores_inner = 1, thresh = 1e-08, size_coll_mod = 10, fast = TRUE, verbose = FALSE, 
+  plot = TRUE)
+  {
+  p <- dim(X)[2]
+  m <- dim(Y)[2]
+  n <- dim(X)[1]
   
-  if (verbose)
+  if (verbose) 
     print("main loop: over all k and all lambda")
   
   if (ncores_outer > 1)
   {
-    cl = parallel::makeCluster(ncores_outer, outfile='')
-    parallel::clusterExport( cl=cl, envir=environment(), varlist=c("X","Y","procedure",
-                                                                   "selecMod","gamma","mini","maxi","eps","kmin","kmax","rank.min","rank.max",
-                                                                   "ncores_outer","ncores_inner","thresh","size_coll_mod","verbose","p","m") )
+    cl <- parallel::makeCluster(ncores_outer, outfile = "")
+    parallel::clusterExport(cl = cl, envir = environment(), varlist = c("X", 
+      "Y", "procedure", "selecMod", "gamma", "mini", "maxi", "eps", "kmin", 
+      "kmax", "rank.min", "rank.max", "ncores_outer", "ncores_inner", "thresh", 
+      "size_coll_mod", "verbose", "p", "m"))
   }
   
   # Compute models with k components
   computeModels <- function(k)
   {
-    if (ncores_outer > 1)
-      require("valse") #nodes start with an empty environment
+    if (ncores_outer > 1) 
+      require("valse")  #nodes start with an empty environment
     
-    if (verbose)
-      print(paste("Parameters initialization for k =",k))
-    #smallEM initializes parameters by k-means and regression model in each component,
-    #doing this 20 times, and keeping the values maximizing the likelihood after 10
-    #iterations of the EM algorithm.
-    P = initSmallEM(k, X, Y)
-    grid_lambda <- computeGridLambda(P$phiInit, P$rhoInit, P$piInit, P$gamInit, X, Y,
-                                     gamma, mini, maxi, eps, fast)
-    if (length(grid_lambda)>size_coll_mod)
-      grid_lambda = grid_lambda[seq(1, length(grid_lambda), length.out = size_coll_mod)]
+    if (verbose) 
+      print(paste("Parameters initialization for k =", k))
+    # smallEM initializes parameters by k-means and regression model in each
+    # component, doing this 20 times, and keeping the values maximizing the
+    # likelihood after 10 iterations of the EM algorithm.
+    P <- initSmallEM(k, X, Y)
+    grid_lambda <- computeGridLambda(P$phiInit, P$rhoInit, P$piInit, P$gamInit, 
+      X, Y, gamma, mini, maxi, eps, fast)
+    if (length(grid_lambda) > size_coll_mod) 
+      grid_lambda <- grid_lambda[seq(1, length(grid_lambda), length.out = size_coll_mod)]
     
-    if (verbose)
+    if (verbose) 
       print("Compute relevant parameters")
-    #select variables according to each regularization parameter
-    #from the grid: S$selected corresponding to selected variables
-    S = selectVariables(P$phiInit, P$rhoInit, P$piInit, P$gamInit, mini, maxi, gamma,
-                        grid_lambda, X, Y, thresh, eps, ncores_inner, fast) 
+    # select variables according to each regularization parameter from the grid:
+    # S$selected corresponding to selected variables
+    S <- selectVariables(P$phiInit, P$rhoInit, P$piInit, P$gamInit, mini, maxi, 
+      gamma, grid_lambda, X, Y, thresh, eps, ncores_inner, fast)
     
-    if (procedure == 'LassoMLE')
+    if (procedure == "LassoMLE")
     {
-      if (verbose)
-        print('run the procedure Lasso-MLE')
-      #compute parameter estimations, with the Maximum Likelihood
-      #Estimator, restricted on selected variables.
-      models <- constructionModelesLassoMLE( P$phiInit, P$rhoInit, P$piInit, P$gamInit, 
-                                            mini, maxi, gamma, X, Y, eps, S, ncores_inner, fast, verbose)
+      if (verbose) 
+        print("run the procedure Lasso-MLE")
+      # compute parameter estimations, with the Maximum Likelihood Estimator,
+      # restricted on selected variables.
+      models <- constructionModelesLassoMLE(P$phiInit, P$rhoInit, P$piInit, 
+        P$gamInit, mini, maxi, gamma, X, Y, eps, S, ncores_inner, fast, verbose)
       
-    }
-    else
+    } else
     {
-      if (verbose)
-        print('run the procedure Lasso-Rank')
-      #compute parameter estimations, with the Low Rank
-      #Estimator, restricted on selected variables.
-      models <- constructionModelesLassoRank(S, k, mini, maxi, X, Y, eps,
-                                             rank.min, rank.max, ncores_inner, fast, verbose)
+      if (verbose) 
+        print("run the procedure Lasso-Rank")
+      # compute parameter estimations, with the Low Rank Estimator, restricted on
+      # selected variables.
+      models <- constructionModelesLassoRank(S, k, mini, maxi, X, Y, eps, rank.min, 
+        rank.max, ncores_inner, fast, verbose)
     }
-    #warning! Some models are NULL after running selectVariables
-    models = models[sapply(models, function(cell) !is.null(cell))]
+    # warning! Some models are NULL after running selectVariables
+    models <- models[sapply(models, function(cell) !is.null(cell))]
     models
   }
   
   # List (index k) of lists (index lambda) of models
-  models_list <-
-    if (ncores_outer > 1)
-      parLapply(cl, kmin:kmax, computeModels)
-  else
-    lapply(kmin:kmax, computeModels)
-  if (ncores_outer > 1)
+  models_list <- if (ncores_outer > 1) 
+    parLapply(cl, kmin:kmax, computeModels) else lapply(kmin:kmax, computeModels)
+  if (ncores_outer > 1) 
     parallel::stopCluster(cl)
   
-  if (! requireNamespace("capushe", quietly=TRUE))
+  if (!requireNamespace("capushe", quietly = TRUE))
   {
     warning("'capushe' not available: returning all models")
-    return (models_list)
+    return(models_list)
   }
   
-  # Get summary "tableauRecap" from models
-  tableauRecap = do.call( rbind, lapply( seq_along(models_list), function(i) {
+  # Get summary 'tableauRecap' from models
+  tableauRecap <- do.call(rbind, lapply(seq_along(models_list), function(i)
+  {
     models <- models_list[[i]]
-    #For a collection of models (same k, several lambda):
-    LLH <- sapply( models, function(model) model$llh[1] )
-    k = length(models[[1]]$pi)
-    sumPen = sapply(models, function(model)
-      k*(dim(model$rho)[1]+sum(model$phi[,,1]!=0)+1)-1)
-    data.frame(model=paste(i,".",seq_along(models),sep=""),
-               pen=sumPen/n, complexity=sumPen, contrast=-LLH)
-  } ) )
+    # For a collection of models (same k, several lambda):
+    LLH <- sapply(models, function(model) model$llh[1])
+    k <- length(models[[1]]$pi)
+    sumPen <- sapply(models, function(model) k * (dim(model$rho)[1] + sum(model$phi[, 
+      , 1] != 0) + 1) - 1)
+    data.frame(model = paste(i, ".", seq_along(models), sep = ""), pen = sumPen/n, 
+      complexity = sumPen, contrast = -LLH)
+  }))
   
   print(tableauRecap)
-  tableauRecap = tableauRecap[which(tableauRecap[,4]!= Inf),]
+  tableauRecap <- tableauRecap[which(tableauRecap[, 4] != Inf), ]
   
-  modSel = capushe::capushe(tableauRecap, n)
-  indModSel <-
-    if (selecMod == 'DDSE')
-      as.numeric(modSel@DDSE@model)
-  else if (selecMod == 'Djump')
-    as.numeric(modSel@Djump@model)
-  else if (selecMod == 'BIC')
-    modSel@BIC_capushe$model
-  else if (selecMod == 'AIC')
+  modSel <- capushe::capushe(tableauRecap, n)
+  indModSel <- if (selecMod == "DDSE") 
+    as.numeric(modSel@DDSE@model) else if (selecMod == "Djump") 
+    as.numeric(modSel@Djump@model) else if (selecMod == "BIC") 
+    modSel@BIC_capushe$model else if (selecMod == "AIC") 
     modSel@AIC_capushe$model
-
-  mod = as.character(tableauRecap[indModSel,1])
-  listMod = as.integer(unlist(strsplit(mod, "[.]")))
-  modelSel = models_list[[listMod[1]]][[listMod[2]]]
-
-  ##Affectations
-  Gam = matrix(0, ncol = length(modelSel$pi), nrow = n)
-  for (i in 1:n){
-    for (r in 1:length(modelSel$pi)){
-      sqNorm2 = sum( (Y[i,]%*%modelSel$rho[,,r]-X[i,]%*%modelSel$phi[,,r])^2 )
-      Gam[i,r] = modelSel$pi[r] * exp(-0.5*sqNorm2)* det(modelSel$rho[,,r])
+  
+  mod <- as.character(tableauRecap[indModSel, 1])
+  listMod <- as.integer(unlist(strsplit(mod, "[.]")))
+  modelSel <- models_list[[listMod[1]]][[listMod[2]]]
+  
+  ## Affectations
+  Gam <- matrix(0, ncol = length(modelSel$pi), nrow = n)
+  for (i in 1:n)
+  {
+    for (r in 1:length(modelSel$pi))
+    {
+      sqNorm2 <- sum((Y[i, ] %*% modelSel$rho[, , r] - X[i, ] %*% modelSel$phi[, 
+        , r])^2)
+      Gam[i, r] <- modelSel$pi[r] * exp(-0.5 * sqNorm2) * det(modelSel$rho[, 
+        , r])
     }
   }
-  Gam = Gam/rowSums(Gam)
-  modelSel$affec = apply(Gam, 1,which.max)
-  modelSel$proba = Gam
-
-  if (plot){
-    print(plot_valse(X,Y,modelSel,n))
+  Gam <- Gam/rowSums(Gam)
+  modelSel$affec <- apply(Gam, 1, which.max)
+  modelSel$proba <- Gam
+  
+  if (plot)
+  {
+    print(plot_valse(X, Y, modelSel, n))
   }
-
+  
   return(modelSel)
 }
diff --git a/pkg/R/plot_valse.R b/pkg/R/plot_valse.R
index 0a6fa9e..6207061 100644
--- a/pkg/R/plot_valse.R
+++ b/pkg/R/plot_valse.R
@@ -1,4 +1,4 @@
-#' Plot
+#' Plot 
 #'
 #' It is a function which plots relevant parameters
 #'
@@ -12,69 +12,85 @@
 #'
 #' @export
 #'
-plot_valse = function(X,Y,model,n, comp = FALSE, k1 = NA, k2 = NA){
+plot_valse <- function(X, Y, model, n, comp = FALSE, k1 = NA, k2 = NA)
+{
   require("gridExtra")
   require("ggplot2")
   require("reshape2")
   require("cowplot")
   
-  K = length(model$pi)
+  K <- length(model$pi)
   ## regression matrices
-  gReg = list()
-  for (r in 1:K){
-    Melt = melt(t((model$phi[,,r])))
-    gReg[[r]] = ggplot(data = Melt, aes(x=Var1, y=Var2, fill=value)) +  geom_tile() + 
-      scale_fill_gradient2(low = "blue", high = "red", mid = "white", midpoint = 0,  space = "Lab") +
-      ggtitle(paste("Regression matrices in cluster",r))
+  gReg <- list()
+  for (r in 1:K)
+  {
+    Melt <- melt(t((model$phi[, , r])))
+    gReg[[r]] <- ggplot(data = Melt, aes(x = Var1, y = Var2, fill = value)) + 
+      geom_tile() + scale_fill_gradient2(low = "blue", high = "red", mid = "white", 
+      midpoint = 0, space = "Lab") + ggtitle(paste("Regression matrices in cluster", 
+      r))
   }
   print(gReg)
   
   ## Differences between two clusters
-  if (comp){
-    if (is.na(k1) || is.na(k)){print('k1 and k2 must be integers, representing the clusters you want to compare')}
-    Melt = melt(t(model$phi[,,k1]-model$phi[,,k2]))
-    gDiff = ggplot(data = Melt, aes(x=Var1, y=Var2, fill=value)) +  geom_tile() + 
-      scale_fill_gradient2(low = "blue", high = "red", mid = "white", midpoint = 0,  space = "Lab") +
-      ggtitle(paste("Difference between regression matrices in cluster",k1, "and", k2))
+  if (comp)
+  {
+    if (is.na(k1) || is.na(k))
+    {
+      print("k1 and k2 must be integers, representing the clusters you want to compare")
+    }
+    Melt <- melt(t(model$phi[, , k1] - model$phi[, , k2]))
+    gDiff <- ggplot(data = Melt, aes(x = Var1, y = Var2, fill = value)) + geom_tile() + 
+      scale_fill_gradient2(low = "blue", high = "red", mid = "white", midpoint = 0, 
+        space = "Lab") + ggtitle(paste("Difference between regression matrices in cluster", 
+      k1, "and", k2))
     print(gDiff)
     
   }
   
   ### Covariance matrices
-  matCov = matrix(NA, nrow = dim(model$rho[,,1])[1], ncol = K)
-  for (r in 1:K){
-    matCov[,r] = diag(model$rho[,,r])
+  matCov <- matrix(NA, nrow = dim(model$rho[, , 1])[1], ncol = K)
+  for (r in 1:K)
+  {
+    matCov[, r] <- diag(model$rho[, , r])
   }
-  MeltCov =  melt(matCov)
-  gCov = ggplot(data =MeltCov, aes(x=Var1, y=Var2, fill=value)) +  geom_tile() + 
-    scale_fill_gradient2(low = "blue", high = "red", mid = "white", midpoint = 0,  space = "Lab") +
-    ggtitle("Covariance matrices")
-  print(gCov )
+  MeltCov <- melt(matCov)
+  gCov <- ggplot(data = MeltCov, aes(x = Var1, y = Var2, fill = value)) + geom_tile() + 
+    scale_fill_gradient2(low = "blue", high = "red", mid = "white", midpoint = 0, 
+      space = "Lab") + ggtitle("Covariance matrices")
+  print(gCov)
   
   ### Proportions
-  gam2 = matrix(NA, ncol = K, nrow = n)
-  for (i in 1:n){
-    gam2[i, ] = c(model$proba[i, model$affec[i]], model$affec[i])
+  gam2 <- matrix(NA, ncol = K, nrow = n)
+  for (i in 1:n)
+  {
+    gam2[i, ] <- c(model$proba[i, model$affec[i]], model$affec[i])
   }
   
-  bp <- ggplot(data.frame(gam2), aes(x=X2, y=X1, color=X2, group = X2)) +
-    geom_boxplot() + theme(legend.position = "none")+ background_grid(major = "xy", minor = "none")
+  bp <- ggplot(data.frame(gam2), aes(x = X2, y = X1, color = X2, group = X2)) + 
+    geom_boxplot() + theme(legend.position = "none") + background_grid(major = "xy", 
+    minor = "none")
   print(bp)
   
   ### Mean in each cluster
-  XY = cbind(X,Y)
-  XY_class= list()
-  meanPerClass= matrix(0, ncol = K, nrow = dim(XY)[2])
-  for (r in 1:K){
-    XY_class[[r]] = XY[model$affec == r, ]
-    if (sum(model$affec==r) == 1){
-      meanPerClass[,r] = XY_class[[r]]
-    } else {
-      meanPerClass[,r] = apply(XY_class[[r]], 2, mean)
+  XY <- cbind(X, Y)
+  XY_class <- list()
+  meanPerClass <- matrix(0, ncol = K, nrow = dim(XY)[2])
+  for (r in 1:K)
+  {
+    XY_class[[r]] <- XY[model$affec == r, ]
+    if (sum(model$affec == r) == 1)
+    {
+      meanPerClass[, r] <- XY_class[[r]]
+    } else
+    {
+      meanPerClass[, r] <- apply(XY_class[[r]], 2, mean)
     }
   }
-  data = data.frame(mean = as.vector(meanPerClass), cluster = as.character(rep(1:K, each = dim(XY)[2])), time = rep(1:dim(XY)[2],K))
-  g = ggplot(data, aes(x=time, y = mean, group = cluster, color = cluster))
-  print(g + geom_line(aes(linetype=cluster, color=cluster))+  geom_point(aes(color=cluster)) + ggtitle('Mean per cluster'))
+  data <- data.frame(mean = as.vector(meanPerClass), cluster = as.character(rep(1:K, 
+    each = dim(XY)[2])), time = rep(1:dim(XY)[2], K))
+  g <- ggplot(data, aes(x = time, y = mean, group = cluster, color = cluster))
+  print(g + geom_line(aes(linetype = cluster, color = cluster)) + geom_point(aes(color = cluster)) + 
+    ggtitle("Mean per cluster"))
   
-}
\ No newline at end of file
+}
diff --git a/pkg/R/selectVariables.R b/pkg/R/selectVariables.R
index 0225287..fe0688c 100644
--- a/pkg/R/selectVariables.R
+++ b/pkg/R/selectVariables.R
@@ -1,19 +1,19 @@
-#' selectVariables
+#' selectVariables 
 #'
 #' It is a function which construct, for a given lambda, the sets of relevant variables.
 #'
 #' @param phiInit an initial estimator for phi (size: p*m*k)
 #' @param rhoInit an initial estimator for rho (size: m*m*k)
-#' @param piInit	an initial estimator for pi (size : k)
+#' @param piInit\tan initial estimator for pi (size : k)
 #' @param gamInit an initial estimator for gamma
-#' @param mini		minimum number of iterations in EM algorithm
-#' @param maxi		maximum number of iterations in EM algorithm
-#' @param gamma	 power in the penalty
+#' @param mini\t\tminimum number of iterations in EM algorithm
+#' @param maxi\t\tmaximum number of iterations in EM algorithm
+#' @param gamma\t power in the penalty
 #' @param glambda grid of regularization parameters
-#' @param X			 matrix of regressors
-#' @param Y			 matrix of responses
+#' @param X\t\t\t matrix of regressors
+#' @param Y\t\t\t matrix of responses
 #' @param thresh real, threshold to say a variable is relevant, by default = 1e-8
-#' @param eps		 threshold to say that EM algorithm has converged
+#' @param eps\t\t threshold to say that EM algorithm has converged
 #' @param ncores Number or cores for parallel execution (1 to disable)
 #'
 #' @return a list of outputs, for each lambda in grid: selected,Rho,Pi
@@ -22,54 +22,53 @@
 #'
 #' @export
 #'
-selectVariables = function(phiInit,rhoInit,piInit,gamInit,mini,maxi,gamma,glambda,
-                           X,Y,thresh=1e-8,eps, ncores=3, fast=TRUE)
-{
+selectVariables <- function(phiInit, rhoInit, piInit, gamInit, mini, maxi, gamma, 
+  glambda, X, Y, thresh = 1e-08, eps, ncores = 3, fast = TRUE)
+  {
   if (ncores > 1)
   {
-    cl = parallel::makeCluster(ncores, outfile='')
-    parallel::clusterExport(cl=cl,
-                            varlist=c("phiInit","rhoInit","gamInit","mini","maxi","glambda","X","Y","thresh","eps"),
-                            envir=environment())
+    cl <- parallel::makeCluster(ncores, outfile = "")
+    parallel::clusterExport(cl = cl, varlist = c("phiInit", "rhoInit", "gamInit", 
+      "mini", "maxi", "glambda", "X", "Y", "thresh", "eps"), envir = environment())
   }
   
   # Computation for a fixed lambda
   computeCoefs <- function(lambda)
   {
-    params = EMGLLF(phiInit,rhoInit,piInit,gamInit,mini,maxi,gamma,lambda,X,Y,eps,fast)
+    params <- EMGLLF(phiInit, rhoInit, piInit, gamInit, mini, maxi, gamma, lambda, 
+      X, Y, eps, fast)
     
-    p = dim(phiInit)[1]
-    m = dim(phiInit)[2]
+    p <- dim(phiInit)[1]
+    m <- dim(phiInit)[2]
     
-    #selectedVariables: list where element j contains vector of selected variables in [1,m]
-    selectedVariables = lapply(1:p, function(j) {
-      #from boolean matrix mxk of selected variables obtain the corresponding boolean m-vector,
-      #and finally return the corresponding indices
-      seq_len(m)[ apply( abs(params$phi[j,,]) > thresh, 1, any ) ]
+    # selectedVariables: list where element j contains vector of selected variables
+    # in [1,m]
+    selectedVariables <- lapply(1:p, function(j)
+    {
+      # from boolean matrix mxk of selected variables obtain the corresponding boolean
+      # m-vector, and finally return the corresponding indices
+      seq_len(m)[apply(abs(params$phi[j, , ]) > thresh, 1, any)]
     })
     
-    list("selected"=selectedVariables,"Rho"=params$rho,"Pi"=params$pi)
+    list(selected = selectedVariables, Rho = params$rho, Pi = params$pi)
   }
   
   # For each lambda in the grid, we compute the coefficients
-  out <-
-    if (ncores > 1)
-      parLapply(cl, glambda, computeCoefs)
-  else
-    lapply(glambda, computeCoefs)
-  if (ncores > 1)
+  out <- if (ncores > 1) 
+    parLapply(cl, glambda, computeCoefs) else lapply(glambda, computeCoefs)
+  if (ncores > 1) 
     parallel::stopCluster(cl)
-  # Suppress models which are computed twice
-  #En fait, ca ca fait la comparaison de tous les parametres
-  #On veut juste supprimer ceux qui ont les memes variables sélectionnées
-  #sha1_array <- lapply(out, digest::sha1)
-  #out[ duplicated(sha1_array) ]
-  selec = lapply(out, function(model) model$selected)
-  ind_dup = duplicated(selec)
-  ind_uniq = which(!ind_dup)
-  out2 = list()
-  for (l in 1:length(ind_uniq)){
-    out2[[l]] = out[[ind_uniq[l]]]
+  # Suppress models which are computed twice En fait, ca ca fait la comparaison de
+  # tous les parametres On veut juste supprimer ceux qui ont les memes variables
+  # sélectionnées sha1_array <- lapply(out, digest::sha1) out[
+  # duplicated(sha1_array) ]
+  selec <- lapply(out, function(model) model$selected)
+  ind_dup <- duplicated(selec)
+  ind_uniq <- which(!ind_dup)
+  out2 <- list()
+  for (l in 1:length(ind_uniq))
+  {
+    out2[[l]] <- out[[ind_uniq[l]]]
   }
   out2
 }