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[valse.git] / pkg / DESCRIPTION

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