Package: valse
Title: Variable Selection with Mixture of Models
-Date: 2020-03-11
+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
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).
+ (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) <arXiv:1409.1333v2>,
+ published in Advances in Data Analysis and Clustering.
Author: Benjamin Auder <benjamin.auder@universite-paris-saclay.fr> [aut,cre],
Emilie Devijver <Emilie.Devijver@kuleuven.be> [aut],
- Benjamin Goehry <Benjamin.Goehry@math.u-psud.fr> [aut]
+ Benjamin Goehry <Benjamin.Goehry@math.u-psud.fr> [ctb]
Maintainer: Benjamin Auder <benjamin.auder@universite-paris-saclay.fr>
Depends:
R (>= 3.5.0)
Imports:
MASS,
parallel,
- ggplot2,
cowplot,
+ ggplot2,
reshape2
Suggests:
capushe,
roxygen2
-URL: http://git.auder.net/?p=valse.git
+URL: https://git.auder.net/?p=valse.git
License: MIT + file LICENSE
-RoxygenNote: 7.0.2
+RoxygenNote: 7.1.1
Collate:
'plot_valse.R'
'main.R'
projects / valse.git / blobdiff