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

Fast OpenMP parallel computing of Breiman's random forests for univariate, multivariate, unsupervised, survival, competing risks, class imbalanced classification and quantile regression. Extreme random forests and randomized splitting. Suite of imputation methods for missing data. Fast random forests using subsampling. Confidence regions and standard errors for variable importance. New improved holdout variable importance. Visualize trees on your Safari or Google Chrome browser.

Version: 2.10.1
Depends: R (≥ 3.6.0)
Imports: parallel, data.tree, DiagrammeR
Suggests: survival, pec, prodlim, mlbench, akima, caret, imbalance, cluster
Published: 2021-02-10
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/ 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_2.10.1.tar.gz
Windows binaries: r-devel: randomForestSRC_2.10.1.zip, r-release: randomForestSRC_2.10.1.zip, r-oldrel: randomForestSRC_2.10.1.zip
macOS binaries: r-release: randomForestSRC_2.10.1.tgz, r-oldrel: randomForestSRC_2.10.1.tgz
Old sources: randomForestSRC archive

Reverse dependencies:

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


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