+#' Main function
+#'
+#' @param X matrix of covariates (of size n*p)
+#' @param Y matrix of responses (of size n*m)
+#' @param procedure among 'LassoMLE' or 'LassoRank'
+#' @param selecMod method to select a model among 'SlopeHeuristic', 'BIC', 'AIC'
+#' @param gamma integer for the power in the penaly, by default = 1
+#' @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 eps real, threshold to say the EM algorithm converges, by default = 1e-4
+#' @param kmin integer, minimum number of clusters, by default = 2
+#' @param kmax integer, maximum number of clusters, by default = 10
+#' @param rang.min integer, minimum rank in the low rank procedure, by default = 1
+#' @param rang.max integer, maximum rank in the
+#' @return a list with estimators of parameters
+#' @export
+#-----------------------------------------------------------------------
+valse = function(X,Y,procedure,selecMod,gamma = 1,mini = 10,
+ maxi = 100,eps = 1e-4,kmin = 2,kmax = 10,
+ rang.min = 1,rang.max = 10) {
+ ##################################
+ #core workflow: compute all models
+ ##################################
+
+ p = dim(phiInit)[1]
+ m = dim(phiInit)[2]
+
+ print("main loop: over all k and all lambda")
+ for (k in kmin:kmax)
+ {
+ print(k)
+
+ print("Parameters initialization")
+ #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.
+ init = initSmallEM(k, X, Y)
+ phiInit <<- init$phiInit
+ rhoInit <<- init$rhoInit
+ piInit <<- init$piInit
+ gamInit <<- init$gamInit
+
+ gridLambda <<- gridLambda(phiInit, rhoInit, piInit, tauInit, X, Y, gamma, mini, maxi, eps)
+
+ print("Compute relevant parameters")
+ #select variables according to each regularization parameter
+ #from the grid: A1 corresponding to selected variables, and
+ #A2 corresponding to unselected variables.
+ params = selectiontotale(phiInit,rhoInit,piInit,tauInit,
+ mini,maxi,gamma,gridLambda,
+ X,Y,thresh,eps)
+ A1 <<- params$A1
+ A2 <<- params$A2
+ Rho <<- params$Rho
+ Pi <<- params$Pi
+
+ if (procedure == 'LassoMLE') {
+ print('run the procedure Lasso-MLE')
+ #compute parameter estimations, with the Maximum Likelihood
+ #Estimator, restricted on selected variables.
+ model = constructionModelesLassoMLE(
+ phiInit, rhoInit,piInit,tauInit,mini,maxi,
+ gamma,gridLambda,X,Y,thresh,eps,A1,A2)
+ ################################################
+ ### Regarder la SUITE
+ r1 = runProcedure1()
+ Phi2 = Phi
+ Rho2 = Rho
+ Pi2 = Pi
+
+ if (is.null(dim(Phi2)))
+ #test was: size(Phi2) == 0
+ {
+ Phi[, , 1:k] <<- r1$phi
+ Rho[, , 1:k] <<- r1$rho
+ Pi[1:k,] <<- r1$pi
+ } else
+ {
+ Phi <<-
+ array(0., dim = c(p, m, kmax, dim(Phi2)[4] + dim(r1$phi)[4]))
+ Phi[, , 1:(dim(Phi2)[3]), 1:(dim(Phi2)[4])] <<- Phi2
+ Phi[, , 1:k, dim(Phi2)[4] + 1] <<- r1$phi
+ Rho <<-
+ array(0., dim = c(m, m, kmax, dim(Rho2)[4] + dim(r1$rho)[4]))
+ Rho[, , 1:(dim(Rho2)[3]), 1:(dim(Rho2)[4])] <<- Rho2
+ Rho[, , 1:k, dim(Rho2)[4] + 1] <<- r1$rho
+ Pi <<- array(0., dim = c(kmax, dim(Pi2)[2] + dim(r1$pi)[2]))
+ Pi[1:nrow(Pi2), 1:ncol(Pi2)] <<- Pi2
+ Pi[1:k, ncol(Pi2) + 1] <<- r1$pi
+ }
+ } else {
+ print('run the procedure Lasso-Rank')
+ #compute parameter estimations, with the Low Rank
+ #Estimator, restricted on selected variables.
+ model = constructionModelesLassoRank(Pi, Rho, mini, maxi, X, Y, eps,
+ A1, rank.min, rank.max)
+
+ ################################################
+ ### Regarder la SUITE
+ phi = runProcedure2()$phi
+ Phi2 = Phi
+ if (dim(Phi2)[1] == 0)
+ {
+ Phi[, , 1:k,] <<- phi
+ } else
+ {
+ Phi <<- array(0, dim = c(p, m, kmax, dim(Phi2)[4] + dim(phi)[4]))
+ Phi[, , 1:(dim(Phi2)[3]), 1:(dim(Phi2)[4])] <<- Phi2
+ Phi[, , 1:k,-(1:(dim(Phi2)[4]))] <<- phi
+ }
+ }
+ }
+ print('Model selection')
+ if (selecMod == 'SlopeHeuristic') {
+
+ } else if (selecMod == 'BIC') {
+
+ } else if (selecMod == 'AIC') {
+
+ }
+}