Package: valse
-Title: Variable selection with mixture of models
+Title: VAriabLe SElection with mixture of models
Date: 2016-12-01
Version: 0.1-0
-Description: TODO
-Authors@R: c( person("Benjamin Auder", "Developer", role=c("ctb","cre"), email="Benjamin.Auder@math.u-psud.fr"),
- person("Benjamin Goehry", "User", role="aut", email = "Benjamin.Goehry@math.u-psud.fr"),
- person("Emilie Devijver", "User", role="ctb", email = "Emilie.Devijver@kuleuven.be"))
+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 <Benjamin.Auder@math.u-psud.fr> [aut,cre],
+ Benjamin Goehry <Benjamin.Goehry@math.u-psud.fr> [aut]
+ Emilie Devijver <Emilie.Devijver@kuleuven.be> [aut]
+Maintainer: Benjamin Auder <Benjamin.Auder@math.u-psud.fr>
Depends:
- R (>= 2.15)
-LazyData: yes
+ R (>= 3.0.0)
+Imports:
+ MASS,
+ methods
+Suggests:
+ parallel,
+ testthat,
+ knitr
URL: http://git.auder.net/?p=valse.git
-License: MIT
+License: MIT + file LICENSE
+VignetteBuilder: knitr
+RoxygenNote: 5.0.1