+++ /dev/null
-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