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: 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)
+ 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],
+ Emilie Devijver <Emilie.Devijver@kuleuven.be> [aut],
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 (>= 3.0.0)
methods
Suggests:
parallel,
- testthat,
- knitr
+ testhat,
+ devtools,
+ rmarkdown
URL: http://git.auder.net/?p=valse.git
License: MIT + file LICENSE
VignetteBuilder: knitr
-RoxygenNote: 5.0.1
+RoxygenNote: 6.0.1