X-Git-Url: https://git.auder.net/?p=valse.git;a=blobdiff_plain;f=pkg%2FDESCRIPTION;h=ed3eb2f01bcf65d379a0681e0d7c2a18a04faa42;hp=3b33e257e2dff5cd51e83eb25595c4e620961156;hb=6382130f19d2de72fed32c91c5431caa6481dbf3;hpb=228ee602a972fcac6177db0d539bf9d0c5fa477f diff --git a/pkg/DESCRIPTION b/pkg/DESCRIPTION index 3b33e25..ed3eb2f 100644 --- a/pkg/DESCRIPTION +++ b/pkg/DESCRIPTION @@ -1,6 +1,6 @@ Package: valse -Title: Variable Selection With Mixture Of Models -Date: 2016-12-01 +Title: Variable Selection with Mixture of Models +Date: 2021-05-16 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 @@ -8,25 +8,28 @@ Description: Two methods are implemented to cluster data with finite mixture 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], + (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. +Author: Benjamin Auder [aut,cre], Emilie Devijver [aut], - Benjamin Goehry [aut] -Maintainer: Benjamin Auder + Benjamin Goehry [ctb] +Maintainer: Benjamin Auder Depends: - R (>= 3.0.0) + R (>= 3.5.0) Imports: MASS, - parallel + parallel, + cowplot, + ggplot2, + reshape2 Suggests: capushe, - roxygen2, - testhat -URL: http://git.auder.net/?p=valse.git + roxygen2 +URL: https://git.auder.net/?p=valse.git License: MIT + file LICENSE -RoxygenNote: 5.0.1 +RoxygenNote: 7.1.1 Collate: 'plot_valse.R' 'main.R'