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>
License: GPL-2
NeedsCompilation: yes
SystemRequirements: C++11
Materials: README
CRAN checks: iRF results


Reference manual: iRF.pdf


Package source: iRF_2.0.0.tar.gz
Windows binaries: r-devel: not available, r-devel-UCRT:, r-release: not available, r-oldrel:
macOS binaries: r-release (arm64): iRF_2.0.0.tgz, r-release (x86_64): iRF_2.0.0.tgz, r-oldrel: iRF_2.0.0.tgz


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