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]