Drop useless files (we will use R 'parallel' package)

[valse.git] / DESCRIPTION

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 Package: valse
-Title: Variable selection with mixture of models
+Title: VAriabLe SElection with mixture of models
 Date: 2016-12-01
 Version: 0.1-0
-Description: TODO
-Authors@R: c( person("Benjamin Auder", "Developer", role=c("ctb","cre"), email="Benjamin.Auder@math.u-psud.fr"),
-       person("Benjamin Goehry", "User", role="aut", email = "Benjamin.Goehry@math.u-psud.fr"),
-       person("Emilie Devijver", "User", role="ctb", email = "Emilie.Devijver@kuleuven.be"))
+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]
+Maintainer: Benjamin Auder <Benjamin.Auder@math.u-psud.fr>
 Depends:
-    R (>= 2.15)
-LazyData: yes
+    R (>= 3.0.0)
+Imports:
+    MASS
+Suggests:
+    parallel,
+    testthat,
+    knitr
 URL: http://git.auder.net/?p=valse.git
-License: MIT
+License: MIT + file LICENSE
+VignetteBuilder: knitr
+RoxygenNote: 5.0.1