#' initialization of the EM algorithm
#'
#' @param k number of components
#' @param X matrix of covariates (of size n*p)
#' @param Y matrix of responses (of size n*m)
#' initialization of the EM algorithm
#'
#' @param k number of components
#' @param X matrix of covariates (of size n*p)
#' @param Y matrix of responses (of size n*m)
+#' @param fast boolean to enable or not the C function call
+#'
+#' @return a list with phiInit (the regression parameter reparametrized),
+#' rhoInit (the covariance parameter reparametrized), piInit (the proportion parameter is the
+#' mixture model), gamInit (the conditional expectation)
initSmallEM <- function(k, X, Y, fast)
{
n <- nrow(X)
initSmallEM <- function(k, X, Y, fast)
{
n <- nrow(X)
if (length(Z_indice) == 1) {
betaInit1[, , r, repet] <- MASS::ginv(crossprod(t(X[Z_indice, ]))) %*%
crossprod(t(X[Z_indice, ]), Y[Z_indice, ])
if (length(Z_indice) == 1) {
betaInit1[, , r, repet] <- MASS::ginv(crossprod(t(X[Z_indice, ]))) %*%
crossprod(t(X[Z_indice, ]), Y[Z_indice, ])
- return(list(phiInit = phiInit, rhoInit = rhoInit, piInit = piInit, gamInit = gamInit))
+ list(phiInit = phiInit, rhoInit = rhoInit, piInit = piInit, gamInit = gamInit)