Variable selection using random forests CRAN Task View: ChemPhys (http://CRAN.R-project.org/view=ChemPhys) CRAN Task View: HighPerformanceComputing (http://CRAN.R-project.org/view=HighPerformanceComputing) CRAN Task View: MachineLearning (http://CRAN.R-project.org/view=MachineLearning) Variable selection from random forests using both backwards variable elimination (for the selection of small sets of non-redundant variables) and selection based on the importance spectrum (somewhat similar to scree plots; for the selection of large, potentially highly-correlated variables). Main applications in high-dimensional data (e.g., microarray data, and other genomics and proteomics applications). You can use rpvm instead of Rmpi if you want but I've only tested with Rmpi. Software Depends: R (>= 2.0.0), randomForest Suggests: snow Enhances: Rmpi Ramon Diaz-Uriarte <rdiaz02@gmail.com> Ramon Diaz-Uriarte <rdiaz02@gmail.com> GPL (>= 2) 2010-10-28 application/tgz http://CRAN.R-project.org/package=varSelRF