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

Fast OpenMP parallel processing for Breiman's random forests for survival, competing risks, regression and classification based on Ishwaran and Kogalur's popular random survival forests (RSF) package. Handles missing data and now includes multivariate, unsupervised forests and quantile regression. New fast interface using subsampling.

Version: 2.7.0
Depends: R (≥ 3.1.0)
Imports: parallel
Suggests: glmnet, survival, pec, prodlim, mlbench, akima, caret
Published: 2018-08-17
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

Downloads:

Reference manual: randomForestSRC.pdf
Package source: randomForestSRC_2.7.0.tar.gz
Windows binaries: r-devel: randomForestSRC_2.7.0.zip, r-release: randomForestSRC_2.7.0.zip, r-oldrel: randomForestSRC_2.7.0.zip
OS X binaries: r-release: randomForestSRC_2.7.0.tgz, r-oldrel: randomForestSRC_2.7.0.tgz
Old sources: randomForestSRC archive

Reverse dependencies:

Reverse depends: ggRandomForests
Reverse imports: boostmtree, sprinter
Reverse suggests: CFC, edarf, IPMRF, mlr, mlrCPO, ModelGood, pec, pmml, riskRegression, spatial.tools, survxai

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