Package: valse
-Title: Variable Selection With Mixture Of Models
-Date: 2016-12-01
+Title: Variable Selection with Mixture of Models
+Date: 2020-03-11
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
(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 <Benjamin.Auder@math.u-psud.fr> [aut,cre],
+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]
-Maintainer: Benjamin Auder <Benjamin.Auder@math.u-psud.fr>
+ Benjamin Goehry <Benjamin.Goehry@math.u-psud.fr> [ctb]
+Maintainer: Benjamin Auder <benjamin.auder@universite-paris-saclay.fr>
Depends:
- R (>= 3.0.0)
+ R (>= 3.5.0)
Imports:
MASS,
- parallel
+ parallel,
+ ggplot2,
+ cowplot,
+ reshape2
Suggests:
capushe,
- roxygen2,
- testhat
+ roxygen2
URL: http://git.auder.net/?p=valse.git
License: MIT + file LICENSE
-RoxygenNote: 5.0.1
+RoxygenNote: 7.1.0
+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'