Package: VSURF
Type: Package
Title: Variable Selection Using Random Forests
Version: 0.5
Date: 2013-05-28
Author: Robin Genuer, Jean-Michel Poggi and Christine Tuleau-Malot
Maintainer: Robin Genuer <Robin.Genuer@isped.u-bordeaux2.fr>
Description: Three steps variable selection procedure based on random
forests. Initially developed to handle high dimensional data
(for which number of variables largely exceeds number of
observations), the package is very versatile and can treat most
dimensions of data, for regression and supervised
classification problems.  First step is dedicated to eliminate
irrelevant variables from the dataset.  Second step aims to
select all variables related to the response for interpretation
purpose.  Third step refines the selection by eliminating
redundancy in the set of variables selected by the second step,
for prediction purpose.
License: GPL (>= 2)
Depends: randomForest, rpart
Packaged: 2013-05-28 14:47:12 UTC; robin
NeedsCompilation: no
Repository: CRAN
Date/Publication: 2013-05-28 17:36:16

Description: Three steps variable selection procedure based on random forests. Initially developed to
handle high dimensional data (for which number of variables largely exceeds number of observations),
the package is very versatile and can treat most dimensions of data, for regression and	supervised
First step is dedicated to eliminate irrelevant variables from the dataset.
Second step aims to select all variables related to the response for interpretation purpose.
Third step refines the selection by eliminating redundancy in the set of variables selected by the

Description: Three steps variable selection procedure based on random forests.
Initially developed to handle high dimensional data (for which number of
variables largely exceeds number of observations), the package is very
versatile and can treat most dimensions of data, for regression and
supervised classification problems. First step is dedicated to eliminate
irrelevant variables from the dataset. Second step aims to select all
variables related to the response for interpretation purpose. Third step
refines the selection by eliminating redundancy in the set of variables
selected by the second step, for prediction purpose.
Collate: 'VSURF.R' 'VSURF.interp.R' 'VSURF.pred.R' 'VSURF.thres.R'