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 [aut,cre], Benjamin Goehry [aut] Emilie Devijver [aut] Maintainer: Benjamin Auder Depends: R (>= 3.0.0) Imports: MASS Suggests: parallel, testthat, knitr URL: http://git.auder.net/?p=valse.git License: MIT + file LICENSE VignetteBuilder: knitr RoxygenNote: 5.0.1