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') and 'dgCMatrix' (R package 'Matrix') can be directly analyzed.

Version: 0.11.2
Depends: R (≥ 3.1)
Imports: Rcpp (≥ 0.11.2), Matrix
LinkingTo: Rcpp, RcppEigen
Suggests: survival, testthat
Published: 2019-03-07
Author: Marvin N. Wright [aut, cre], Stefan Wager [ctb], Philipp Probst [ctb]
Maintainer: Marvin N. Wright <cran at wrig.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


Reference manual: ranger.pdf
Package source: ranger_0.11.2.tar.gz
Windows binaries: r-devel: ranger_0.11.2.zip, r-release: ranger_0.11.2.zip, r-oldrel: ranger_0.11.2.zip
OS X binaries: r-release: ranger_0.11.2.tgz, r-oldrel: ranger_0.11.2.tgz
Old sources: ranger archive

Reverse dependencies:

Reverse depends: Boruta, metaforest, tuneRanger
Reverse imports: abcrf, AmyloGram, banter, CaseBasedReasoning, CornerstoneR, healthcareai, missRanger, MlBayesOpt, mopa, OOBCurve, poolVIM, quantregRanger, riskRegression, rmweather, sambia, SCORPIUS, simPop, solitude, spm, tsensembler, VIM
Reverse suggests: batchtools, breakDown, bWGR, cattonum, climbeR, edarf, forestControl, GSIF, iBreakDown, iml, IPMRF, knockoff, lime, MachineShop, MFKnockoffs, mlr, mlrCPO, parsnip, pdp, purge, r2pmml, SuperLearner, superml, tidypredict, vip


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