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.
'VSURF.R' 'VSURF.interp.R' 'VSURF.pred.R' 'VSURF.thres.R'
'plot.VSURF.R' 'summary.VSURF.R' 'toys.R' 'tune.R'
'print.VSURF.R'

c(person("Robin", "Genuer", email = "Robin.Genuer@isped.u-bordeaux2.fr", role = c("aut", "cre")),
person("Jean-Michel", "Poggi", role = "aut"),
person("Christine", "Tuleau-Malot", role = "aut"))
doParallel, foreach, parallel, randomForest, rpart
Robin Genuer [aut, cre],
Jean-Michel Poggi [aut],
Christine Tuleau-Malot [aut]

c(person("Robin", "Genuer", email = "Robin.Genuer@u-bordeaux.fr", role = c("aut", "cre")),
person("Jean-Michel", "Poggi", role = "aut"),
person("Christine", "Tuleau-Malot", role = "aut"))
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.
Genuer, R. and Poggi, J.M. and Tuleau-Malot, C. (2015)
<https://journal.r-project.org/archive/2015-2/genuer-poggi-tuleaumalot.pdf>.