X-Git-Url: https://git.auder.net/?p=valse.git;a=blobdiff_plain;f=pkg%2FDESCRIPTION;h=3b33e257e2dff5cd51e83eb25595c4e620961156;hp=9d8a67754bf821a1e8295f560d229e3aa815486d;hb=e32621012b1660204434a56acc8cf73eac42f477;hpb=f33f35efc9a01f93bb61959522d90ee6a76b892e

diff --git a/pkg/DESCRIPTION b/pkg/DESCRIPTION
deleted file mode 100644
index 9d8a677..0000000
--- a/pkg/DESCRIPTION
+++ /dev/null
@@ -1,28 +0,0 @@
-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],
-    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 (>= 3.0.0)
-Imports:
-    MASS,
-    methods
-Suggests:
-    parallel,
-    testthat,
-    knitr
-URL: http://git.auder.net/?p=valse.git
-License: MIT + file LICENSE
-VignetteBuilder: knitr
-RoxygenNote: 5.0.1