Adjustments for CRAN upload

[valse.git] / pkg / R / computeGridLambda.R

diff --git a/pkg/R/computeGridLambda.R b/pkg/R/computeGridLambda.R
index ac0788a..f4073d0 100644 (file)
--- a/pkg/R/computeGridLambda.R
+++ b/pkg/R/computeGridLambda.R
@@ -1,4 +1,4 @@
-#' computeGridLambda 
+#' computeGridLambda
 #'
 #' Construct the data-driven grid for the regularization parameters used for the Lasso estimator
 #'
@@ -12,11 +12,13 @@
 #' @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
+#' @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, 
+computeGridLambda <- function(phiInit, rhoInit, piInit, gamInit, X, Y, gamma, mini,
   maxi, eps, fast)
 {
   n <- nrow(X)
@@ -24,7 +26,7 @@ computeGridLambda <- function(phiInit, rhoInit, piInit, gamInit, X, Y, gamma, mi
   m <- ncol(Y)
   k <- length(piInit)
 
-  list_EMG <- EMGLLF(phiInit, rhoInit, piInit, gamInit, mini, maxi, gamma, lambda = 0, 
+  list_EMG <- EMGLLF(phiInit, rhoInit, piInit, gamInit, mini, maxi, gamma, lambda = 0,
     X, Y, eps, fast)
 
   grid <- array(0, dim = c(p, m, k))