X-Git-Url: https://git.auder.net/?p=valse.git;a=blobdiff_plain;f=DESCRIPTION;h=f8f5a2960383fe99bd07c2e038108aab5802ca33;hp=07caf926d64a70c8ff08c6b21330c63f9fe7fba6;hb=7f1a6cf08a4d4d67e8a95b8c1c0cc74ff3deb5a4;hpb=f2a9120810d7e1e423c7b5c2c4320f4e27221f50 diff --git a/DESCRIPTION b/DESCRIPTION index 07caf92..f8f5a29 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,14 +1,27 @@ 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 [aut,cre], + Benjamin Goehry [aut] + Emilie Devijver [aut] +Maintainer: Benjamin Auder Depends: - R (>= 2.15) -LazyData: yes + R (>= 3.0.0) +Imports: + MASS +Suggests: + parallel, + testthat, + knitr URL: http://git.auder.net/?p=valse.git -License: MIT +License: MIT + file LICENSE +VignetteBuilder: knitr RoxygenNote: 5.0.1