DESCRIPTION

   1 Package: valse
   2 Title: VAriabLe SElection with mixture of models
   3 Date: 2016-12-01
   4 Version: 0.1-0
   5 Description: Two methods are implemented to cluster data with finite mixture regression models.
   6               Those procedures deal with high-dimensional covariates and responses through a variable selection
   7               procedure based on the Lasso estimator. A low-rank constraint could be added, computed for the Lasso-Rank procedure.
   8               A collection of models is constructed, varying the level of sparsity and the number of clusters, and a model is selected
   9               using a model selection criterion (slope heuristic, BIC or AIC).
  10               Details of the procedure are provided in 'Model-based clustering for high-dimensional data. Application to functional data'
  11               by Emilie Devijver, published in Advances in Data Analysis and Clustering (2016)
  12 Author: Benjamin Auder <Benjamin.Auder@math.u-psud.fr> [aut,cre],
  13     Benjamin Goehry <Benjamin.Goehry@math.u-psud.fr> [aut]
  14     Emilie Devijver <Emilie.Devijver@kuleuven.be> [aut]
  15 Maintainer: Benjamin Auder <Benjamin.Auder@math.u-psud.fr>
  16 Depends:
  17     R (>= 3.0.0)
  18 Imports:
  19     MASS,
  20     methods
  21 Suggests:
  22     parallel,
  23     testthat,
  24     knitr
  25 URL: http://git.auder.net/?p=valse.git
  26 License: MIT + file LICENSE
  27 VignetteBuilder: knitr
  28 RoxygenNote: 5.0.1