| 1 | Package: valse |
| 2 | Title: Variable Selection With Mixture Of Models |
| 3 | Date: 2016-12-01 |
| 4 | Version: 0.1-0 |
| 5 | Description: Two methods are implemented to cluster data with finite mixture regression models. |
| 6 | Those procedures deal with high-dimensional covariates and responses through a variable |
| 7 | selection procedure based on the Lasso estimator. A low-rank constraint could be added, |
| 8 | computed for the Lasso-Rank procedure. |
| 9 | A collection of models is constructed, varying the level of sparsity and the number of |
| 10 | clusters, and a model is selected using a model selection criterion (slope heuristic, |
| 11 | BIC or AIC). Details of the procedure are provided in 'Model-based clustering for |
| 12 | high-dimensional data. Application to functional data' by Emilie Devijver, published in |
| 13 | Advances in Data Analysis and Clustering (2016). |
| 14 | Author: Benjamin Auder <Benjamin.Auder@math.u-psud.fr> [aut,cre], |
| 15 | Emilie Devijver <Emilie.Devijver@kuleuven.be> [aut], |
| 16 | Benjamin Goehry <Benjamin.Goehry@math.u-psud.fr> [aut] |
| 17 | Maintainer: Benjamin Auder <Benjamin.Auder@math.u-psud.fr> |
| 18 | Depends: |
| 19 | R (>= 3.0.0) |
| 20 | Imports: |
| 21 | MASS, |
| 22 | methods |
| 23 | Suggests: |
| 24 | parallel, |
| 25 | testhat, |
| 26 | devtools, |
| 27 | rmarkdown |
| 28 | URL: http://git.auder.net/?p=valse.git |
| 29 | License: MIT + file LICENSE |
| 30 | VignetteBuilder: knitr |
| 31 | RoxygenNote: 5.0.1 |