ranger: A Fast Implementation of Random Forests

A fast implementation of Random Forests, particularly suited for high dimensional data. Ensembles of classification, regression, survival and probability prediction trees are supported. Data from genome-wide association studies can be analyzed efficiently. In addition to data frames, datasets of class 'gwaa.data' (R package 'GenABEL') can be directly analyzed.

Version: 0.7.0
Depends: R (≥ 3.1)
Imports: Rcpp (≥ 0.11.2)
LinkingTo: Rcpp
Suggests: survival, testthat, GenABEL
Published: 2017-03-31
Author: Marvin N. Wright
Maintainer: Marvin N. Wright <wright at imbs.uni-luebeck.de>
BugReports: https://github.com/imbs-hl/ranger/issues
License: GPL-3
URL: https://github.com/imbs-hl/ranger
NeedsCompilation: yes
Citation: ranger citation info
Materials: NEWS
In views: MachineLearning, Survival
CRAN checks: ranger results

Downloads:

Reference manual: ranger.pdf
Package source: ranger_0.7.0.tar.gz
Windows binaries: r-devel: ranger_0.7.0.zip, r-release: ranger_0.7.0.zip, r-oldrel: ranger_0.7.0.zip
OS X El Capitan binaries: r-release: ranger_0.7.0.tgz
OS X Mavericks binaries: r-oldrel: ranger_0.7.0.tgz
Old sources: ranger archive

Reverse dependencies:

Reverse depends: Boruta
Reverse imports: abcrf, AmyloGram, healthcareai, OOBCurve, ordinalForest, simPop
Reverse suggests: batchtools, bWGR, climbeR, edarf, GSIF, mlr, NAM, pdp, purge

Linking:

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