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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,
-    parallel
-Suggests:
-    capushe,
-    roxygen2,
-    testhat
-URL: http://git.auder.net/?p=valse.git
-License: MIT + file LICENSE
-RoxygenNote: 5.0.1
-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'