+++ /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,
- parallel
-Suggests:
- capushe,
- roxygen2,
- testhat
-URL: http://git.auder.net/?p=valse.git
-License: MIT + file LICENSE
-RoxygenNote: 5.0.1
-Collate:
- 'plot.R'
- 'main.R'
- 'selectVariables.R'
- 'constructionModelesLassoRank.R'
- 'constructionModelesLassoMLE.R'
- 'computeGridLambda.R'
- 'initSmallEM.R'
- 'EMGrank.R'
- 'EMGLLF.R'
- 'generateXY.R'
- 'A_NAMESPACE.R'
- 'plot_valse.R'