Variable Selection with Mixture of Models

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 (2016) , published in Advances in Data Analysis and Clustering.


Reference manual

It appears you don't have a PDF plugin for this browser. You can click here to download the reference manual.


0.1-0 by Benjamin Auder, 8 months ago

Browse source code at

Authors: Benjamin Auder <[email protected]> [aut,cre] , Emilie Devijver <[email protected]> [aut] , Benjamin Goehry <[email protected]> [ctb]

Documentation:   PDF Manual  

MIT + file LICENSE license

Imports MASS, parallel, cowplot, ggplot2, reshape2

Suggests capushe, roxygen2

See at CRAN