X-Git-Url: https://git.auder.net/?p=valse.git;a=blobdiff_plain;f=pkg%2FDESCRIPTION;h=ed3eb2f01bcf65d379a0681e0d7c2a18a04faa42;hp=ac463663660720f58de436f9cb0d13d1edf2bff7;hb=6382130f19d2de72fed32c91c5431caa6481dbf3;hpb=d57c255b4a437a5e9afb4ff1b939282944c18eb5

diff --git a/pkg/DESCRIPTION b/pkg/DESCRIPTION
index ac46366..ed3eb2f 100644
--- a/pkg/DESCRIPTION
+++ b/pkg/DESCRIPTION
@@ -1,6 +1,6 @@
 Package: valse
 Title: Variable Selection with Mixture of Models
-Date: 2020-03-11
+Date: 2021-05-16
 Version: 0.1-0
 Description: Two methods are implemented to cluster data with finite mixture
     regression models. Those procedures deal with high-dimensional covariates and
@@ -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]
@@ -20,15 +21,15 @@ Depends:
 Imports:
     MASS,
     parallel,
-    ggplot2,
     cowplot,
+    ggplot2,
     reshape2
 Suggests:
     capushe,
     roxygen2
-URL: http://git.auder.net/?p=valse.git
+URL: https://git.auder.net/?p=valse.git
 License: MIT + file LICENSE
-RoxygenNote: 7.0.2
+RoxygenNote: 7.1.1
 Collate:
     'plot_valse.R'
     'main.R'