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

Fast OpenMP parallel computing of 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, quantile regression and solutions for class imbalanced data. New fast interface using subsampling and confidence regions for variable importance.

Version: 2.9.3
Depends: R (≥ 3.5.0)
Imports: parallel
Suggests: survival, pec, prodlim, mlbench, akima, caret, imbalance
Published: 2020-01-21
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_2.9.3.tar.gz
Windows binaries: r-devel: randomForestSRC_2.9.3.zip, r-release: randomForestSRC_2.9.3.zip, r-oldrel: randomForestSRC_2.9.3.zip
macOS binaries: r-release: randomForestSRC_2.9.3.tgz, r-oldrel: randomForestSRC_2.9.3.tgz
Old sources: randomForestSRC archive

Reverse dependencies:

Reverse depends: ggRandomForests
Reverse imports: boostmtree, fsMTS, SIMMS, subscreen
Reverse suggests: CFC, IPMRF, LTRCforests, mlr, mlrCPO, ModelGood, pec, pmml, riskRegression


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