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.