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-Package: valse
-Title: Variable Selection With Mixture Of Models
-Date: 2016-12-01
-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],
-    Emilie Devijver <Emilie.Devijver@kuleuven.be> [aut],
-    Benjamin Goehry <Benjamin.Goehry@math.u-psud.fr> [aut]
-Maintainer: Benjamin Auder <Benjamin.Auder@math.u-psud.fr>
-Depends:
-    R (>= 3.0.0)
-Imports:
-    MASS,
-    methods
-Suggests:
-    parallel,
-    testhat,
-    devtools,
-    rmarkdown
-URL: http://git.auder.net/?p=valse.git
-License: MIT + file LICENSE
-VignetteBuilder: knitr
-RoxygenNote: 5.0.1