Update to conform to CRAN rules (unfinished)

diff --git a/pkg/DESCRIPTION b/pkg/DESCRIPTION
index 13fee37..ed3eb2f 100644 (file)
--- a/pkg/DESCRIPTION
+++ b/pkg/DESCRIPTION
@@ -8,9 +8,10 @@ Description: Two methods are implemented to cluster data with finite mixture
     A low-rank constraint could be added, computed for the Lasso-Rank procedure.
     A collection of models is constructed, varying the level of sparsity and the
     number of clusters, and a model is selected using a model selection criterion
-    (slope heuristic, BIC or AIC). Details of the procedure are provided in 'Model-
-    based clustering for high-dimensional data. Application to functional data' by
-    Emilie Devijver, published in Advances in Data Analysis and Clustering (2016).
+    (slope heuristic, BIC or AIC). Details of the procedure are provided in
+    "Model-based clustering for high-dimensional data. Application to functional data"
+    by Emilie Devijver (2016) <arXiv:1409.1333v2>,
+    published in Advances in Data Analysis and Clustering.
 Author: Benjamin Auder <benjamin.auder@universite-paris-saclay.fr> [aut,cre],
     Emilie Devijver <Emilie.Devijver@kuleuven.be> [aut],
     Benjamin Goehry <Benjamin.Goehry@math.u-psud.fr> [ctb]

diff --git a/pkg/R/initSmallEM.R b/pkg/R/initSmallEM.R
index 10cb191..cf31c63 100644 (file)
--- a/pkg/R/initSmallEM.R
+++ b/pkg/R/initSmallEM.R
@@ -1,3 +1,5 @@
+#' initSmallEM
+#'
 #' initialization of the EM algorithm
 #'
 #' @param k number of components
@@ -8,6 +10,7 @@
 #' @return a list with phiInit, rhoInit, piInit, gamInit
 #'
 #' @importFrom stats cutree dist hclust runif
+#'
 #' @export
 initSmallEM <- function(k, X, Y, fast)
 {

diff --git a/pkg/R/plot_valse.R b/pkg/R/plot_valse.R
index e3fd38e..0ef5f72 100644 (file)
--- a/pkg/R/plot_valse.R
+++ b/pkg/R/plot_valse.R
@@ -1,7 +1,8 @@
 utils::globalVariables(c("Var1","Var2","X1","X2","value")) #, package="valse")
+
 #' Plot
 #'
-#' It is a function which plots relevant parameters
+#' A function which plots relevant parameters.
 #'
 #' @param X matrix of covariates (of size n*p)
 #' @param Y matrix of responses (of size n*m)
@@ -14,6 +15,8 @@ utils::globalVariables(c("Var1","Var2","X1","X2","value")) #, package="valse")
 #' @importFrom cowplot background_grid
 #' @importFrom reshape2 melt
 #'
+#' @return No return value (only plotting).
+#'
 #' @export
 plot_valse <- function(X, Y, model, comp = FALSE, k1 = NA, k2 = NA)
 {