Package: valse Title: Variable Selection With Mixture Of Models Date: 2020-01-11 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 [aut,cre], Emilie Devijver [aut], Benjamin Goehry [aut] Maintainer: Benjamin Auder Depends: R (>= 3.5.0) Imports: MASS, parallel Suggests: capushe, methods, roxygen2, testthat URL: http://git.auder.net/?p=valse.git License: MIT + file LICENSE RoxygenNote: 7.0.2 Collate: 'plot_valse.R' 'main.R' 'selectVariables.R' 'constructionModelesLassoRank.R' 'constructionModelesLassoMLE.R' 'computeGridLambda.R' 'initSmallEM.R' 'EMGrank.R' 'EMGLLF.R' 'generateXY.R' 'A_NAMESPACE.R' 'util.R'