randomForestSRC: Random Forests for Survival, Regression, and Classification (RF-SRC)

A unified treatment of Breiman's random forests for survival, regression and classification problems based on Ishwaran and Kogalur's random survival forests (RSF) package. Now extended to include multivariate and unsupervised forests. Also includes quantile regression forests for univariate and multivariate training/testing settings. The package runs in both serial and parallel (OpenMP) modes.

Version: 2.5.0
Depends: R (≥ 3.1.0)
Imports: parallel
Suggests: glmnet, survival, pec, prodlim, mlbench
Published: 2017-08-07
Author: Hemant Ishwaran, Udaya B. Kogalur
Maintainer: Udaya B. Kogalur <ubk at kogalur.com>
BugReports: https://github.com/kogalur/randomForestSRC/issues/new
License: GPL (≥ 3)
URL: http://web.ccs.miami.edu/~hishwaran http://www.kogalur.com https://github.com/kogalur/randomForestSRC
NeedsCompilation: yes
Citation: randomForestSRC citation info
Materials: NEWS
In views: HighPerformanceComputing, MachineLearning, Survival
CRAN checks: randomForestSRC results


Reference manual: randomForestSRC.pdf
Package source: randomForestSRC_2.5.0.tar.gz
Windows binaries: r-devel: randomForestSRC_2.5.0.zip, r-release: randomForestSRC_2.5.0.zip, r-oldrel: randomForestSRC_2.5.0.zip
OS X El Capitan binaries: r-release: randomForestSRC_2.5.0.tgz
OS X Mavericks binaries: r-oldrel: randomForestSRC_2.5.0.tgz
Old sources: randomForestSRC archive

Reverse dependencies:

Reverse depends: ggRandomForests
Reverse imports: boostmtree, fifer, sprinter, SurvRank
Reverse suggests: CFC, edarf, IPMRF, mlr, ModelGood, pec, pmml, riskRegression, RLT


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