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. New Mahalanobis splitting for correlated outcomes. 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 importance. Case-specific importance. Minimal depth variable importance. Visualize trees on your Safari or Google Chrome browser. Anonymous random forests for data privacy.

Version:	3.5.1
Depends:	R (≥ 4.3.0)
Imports:	parallel, data.tree, DiagrammeR
Suggests:	survival, pec, prodlim, mlbench, interp, caret, cluster, fst, data.table
Published:	2026-02-12
DOI:	10.32614/CRAN.package.randomForestSRC
Author:	Hemant Ishwaran [aut], Udaya B. Kogalur [aut, cre]
Maintainer:	Udaya B. Kogalur <ubk at kogalur.com>
BugReports:	https://github.com/kogalur/randomForestSRC/issues/
License:	GPL (≥ 3)
URL:	https://www.randomforestsrc.org/ https://ishwaran.org/
NeedsCompilation:	yes
Citation:	randomForestSRC citation info
Materials:	NEWS
In views:	HighPerformanceComputing, MachineLearning, Survival
CRAN checks:	randomForestSRC results

Documentation:

Reference manual:

randomForestSRC.html , randomForestSRC.pdf

Downloads:

Package source:	randomForestSRC_3.5.1.tar.gz
Windows binaries:	r-devel: randomForestSRC_3.5.1.zip, r-release: randomForestSRC_3.5.1.zip, r-oldrel: randomForestSRC_3.5.1.zip
macOS binaries:	r-release (arm64): randomForestSRC_3.5.1.tgz, r-oldrel (arm64): randomForestSRC_3.5.1.tgz, r-release (x86_64): randomForestSRC_3.5.1.tgz, r-oldrel (x86_64): randomForestSRC_3.5.1.tgz
Old sources:	randomForestSRC archive

Reverse dependencies:

Reverse imports:	AutoScore, cjbart, CoxAIPW, CPSM, E2E, glmnetr, pacheck, precmed, ranktreeEnsemble, SIMMS, survcompare, survivalSL, tehtuner, varPro
Reverse suggests:	ClassifyR, familiar, MachineShop, mlrCPO, multipleOutcomes, PheCAP, riskRegression, survex, SurvMetrics, targeted, tram
Reverse enhances:	pec

Linking:

Please use the canonical form https://CRAN.R-project.org/package=randomForestSRC to link to this page.