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 TuleauMalot, C. (2015)
Package details 


Author  Robin Genuer [aut, cre], JeanMichel Poggi [aut], Christine TuleauMalot [aut] 
Date of publication  20180410 10:08:41 UTC 
Maintainer  Robin Genuer <[email protected]> 
License  GPL (>= 2) 
Version  1.0.4 
URL  https://github.com/robingenuer/VSURF 
Package repository  View on CRAN 
Installation 
Install the latest version of this package by entering the following in R:

Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.