#' computeGridLambda #' #' Construct the data-driven grid for the regularization parameters used for the Lasso estimator #' #' @param phiInit value for phi #' @param rhoInit for rho #' @param piInit 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 eps threshold to stop EM algorithm #' @param fast boolean to enable or not the C function call #' #' @return the grid of regularization parameters for the Lasso estimator. The output is a vector with nonnegative values that are relevant #' to be considered as regularization parameter as they are equivalent to a 0 in the regression parameter. #' #' @export computeGridLambda <- function(phiInit, rhoInit, piInit, gamInit, X, Y, gamma, mini, maxi, eps, fast) { n <- nrow(X) p <- ncol(X) m <- ncol(Y) k <- length(piInit) list_EMG <- EMGLLF(phiInit, rhoInit, piInit, gamInit, mini, maxi, gamma, lambda = 0, X, Y, eps, fast) grid <- array(0, dim = c(p, m, k)) for (j in 1:p) { for (mm in 1:m) grid[j, mm, ] <- abs(list_EMG$S[j, mm, ])/(n * list_EMG$pi^gamma) } sort(unique(grid)) }