X-Git-Url: https://git.auder.net/?a=blobdiff_plain;f=DESCRIPTION;h=9d8a67754bf821a1e8295f560d229e3aa815486d;hb=f227455a1604906b255ef366d64c10a93e796983;hp=cdda4e41b73baf3f78aaf4cb71123a264d16268c;hpb=ef67d338c7f28ba041abe40ca9a8ab69f8365a90;p=valse.git

diff --git a/DESCRIPTION b/DESCRIPTION
index cdda4e4..9d8a677 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -2,7 +2,13 @@ Package: valse
 Title: VAriabLe SElection with mixture of models
 Date: 2016-12-01
 Version: 0.1-0
-Description: TODO
+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]
@@ -10,7 +16,8 @@ Maintainer: Benjamin Auder <Benjamin.Auder@math.u-psud.fr>
 Depends:
     R (>= 3.0.0)
 Imports:
-    MASS
+    MASS,
+    methods
 Suggests:
     parallel,
     testthat,