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