+#' 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 gamInit value for gamma
+#' @param X matrix of covariates (of size n*p)
+#' @param Y matrix of responses (of size n*m)
+#' @param gamma power of weights in the penalty
+#' @param mini minimum number of iterations in EM algorithm
+#' @param maxi maximum number of iterations in EM algorithm
+#' @param tau threshold to stop EM algorithm
+#' @return the grid of regularization parameters
+#' @export
+#-----------------------------------------------------------------------
gridLambda = function(phiInit, rhoInit, piInit, gamInit, X, Y, gamma, mini, maxi, tau)
{
n = nrow(X)
m = dim(phiInit)[2]
k = dim(phiInit)[3]
- list_EMG = .Call("EMGLLF",phiInit,rhoInit,piInit,gamInit,mini,maxi,1,0,X,Y,tau)
+ list_EMG = .Call("EMGLLF_core",phiInit,rhoInit,piInit,gamInit,mini,maxi,1,0,X,Y,tau)
grid = array(0, dim=c(p,m,k))
for (i in 1:p)