iterative Random Forests

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

The R package iRF implements iterative Random Forests, a method for iteratively growing ensemble of weighted decision trees, and detecting high-order feature interactions by analyzing feature usage on decision paths. This version uses source codes from the R package randomForest by Andy Liaw and Matthew Weiner and the original Fortran codes by Leo Breiman and Adele Cutler.

To download and install the package, use devtools


You can subsequently load the package with the usual R commands:


OSX users may need to intall gfortran to compile. This can be done with the following commands:

curl -O
sudo tar fvxz gfortran-4.8.2-darwin13.tar.bz2 -C /

Binaries are available for OSX and linux in the binaries directory and can be installed using the command:

R CMD INSTALL <filename>

For a detailed description on the usage of iRF, see the vignette.


Reference manual

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2.0.0 by Karl Kumbier, 4 years ago

Browse source code at

Authors: 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)

Documentation:   PDF Manual  

GPL-2 license

Imports AUC, Matrix, data.table, dplyr, Rcpp, methods, foreach, doParallel, RColorBrewer

Suggests MASS, rgl

Linking to Rcpp

System requirements: C++11

See at CRAN