X-Git-Url: https://git.auder.net/?p=valse.git;a=blobdiff_plain;f=DESCRIPTION;h=f8f5a2960383fe99bd07c2e038108aab5802ca33;hp=cdda4e41b73baf3f78aaf4cb71123a264d16268c;hb=22d21a222df140221657af24d71fe05af54a6adc;hpb=c366645be35213c98df336216864543d171fdb93

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@@ -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]