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
Suggests:
    parallel,
    testthat,
    knitr
URL: http://git.auder.net/?p=valse.git
License: MIT + file LICENSE
VignetteBuilder: knitr
RoxygenNote: 5.0.1