iRF: iterative Random Forests

Iteratively grows feature weighted random forests and finds high-order feature interactions in a stable fashion.

Version: 2.0.0
Depends: R (≥ 3.1.2)
Imports: AUC, Matrix, data.table, dplyr, Rcpp, methods, foreach, doParallel, RColorBrewer
LinkingTo: Rcpp
Suggests: MASS, rgl
Published: 2017-07-26
Author: Sumanta Basu and Karl Kumbier (based on source codes from the R packages FSInteract by Hyun Jik Kim and Rajen D. Shah, randomForest by Andy Liaw and Matthew Wiener, and the original Fortran codes by Leo Breiman and Adele Cutler)
Maintainer: Karl Kumbier <kkumbier at berkeley.edu>
License: GPL-2
URL: https://arxiv.org/abs/1706.08457
NeedsCompilation: yes
SystemRequirements: C++11
Materials: README
CRAN checks: iRF results

Downloads:

Reference manual: iRF.pdf
Package source: iRF_2.0.0.tar.gz
Windows binaries: r-prerel: iRF_2.0.0.zip, r-release: iRF_2.0.0.zip, r-oldrel: iRF_2.0.0.zip
OS X binaries: r-prerel: iRF_2.0.0.tgz, r-release: iRF_2.0.0.tgz

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