X-Git-Url: https://git.auder.net/?p=valse.git;a=blobdiff_plain;f=pkg%2FDESCRIPTION;h=ac463663660720f58de436f9cb0d13d1edf2bff7;hp=9d8a67754bf821a1e8295f560d229e3aa815486d;hb=d57c255b4a437a5e9afb4ff1b939282944c18eb5;hpb=f87ff0f5116c0c1c59c5608e46563ff0f79e5d43

diff --git a/pkg/DESCRIPTION b/pkg/DESCRIPTION
index 9d8a677..ac46366 100644
--- a/pkg/DESCRIPTION
+++ b/pkg/DESCRIPTION
@@ -1,28 +1,44 @@
 Package: valse
-Title: VAriabLe SElection with mixture of models
-Date: 2016-12-01
+Title: Variable Selection with Mixture of Models
+Date: 2020-03-11
 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 responses through a variable selection
-              procedure based on the Lasso estimator. 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)
-Author: Benjamin Auder <Benjamin.Auder@math.u-psud.fr> [aut,cre],
-    Benjamin Goehry <Benjamin.Goehry@math.u-psud.fr> [aut]
-    Emilie Devijver <Emilie.Devijver@kuleuven.be> [aut]
-Maintainer: Benjamin Auder <Benjamin.Auder@math.u-psud.fr>
+Description: Two methods are implemented to cluster data with finite mixture
+    regression models. Those procedures deal with high-dimensional covariates and
+    responses through a variable selection procedure based on the Lasso estimator.
+    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).
+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]
+Maintainer: Benjamin Auder <benjamin.auder@universite-paris-saclay.fr>
 Depends:
-    R (>= 3.0.0)
+    R (>= 3.5.0)
 Imports:
     MASS,
-    methods
-Suggests:
     parallel,
-    testthat,
-    knitr
+    ggplot2,
+    cowplot,
+    reshape2
+Suggests:
+    capushe,
+    roxygen2
 URL: http://git.auder.net/?p=valse.git
 License: MIT + file LICENSE
-VignetteBuilder: knitr
-RoxygenNote: 5.0.1
+RoxygenNote: 7.0.2
+Collate:
+    'plot_valse.R'
+    'main.R'
+    'selectVariables.R'
+    'constructionModelesLassoRank.R'
+    'constructionModelesLassoMLE.R'
+    'computeGridLambda.R'
+    'initSmallEM.R'
+    'EMGrank.R'
+    'EMGLLF.R'
+    'generateXY.R'
+    'A_NAMESPACE.R'
+    'util.R'