X-Git-Url: https://git.auder.net/?p=valse.git;a=blobdiff_plain;f=pkg%2FDESCRIPTION;h=3b33e257e2dff5cd51e83eb25595c4e620961156;hp=8ec8b8ae06d429a06a8bbb34c3de6eab5d736695;hb=e32621012b1660204434a56acc8cf73eac42f477;hpb=0eb161e3f3d018bce7d98fc85622d14910f89d43

diff --git a/pkg/DESCRIPTION b/pkg/DESCRIPTION
deleted file mode 100644
index 8ec8b8a..0000000
--- a/pkg/DESCRIPTION
+++ /dev/null
@@ -1,29 +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],
-    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