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: 3.3.0
Depends: R (≥ 3.6.0)
Imports: parallel, data.tree, DiagrammeR
Suggests: survival, pec, prodlim, mlbench, interp, caret, imbalance, cluster, fst, data.table
Published: 2024-06-25
DOI: 10.32614/CRAN.package.randomForestSRC
Author: Hemant Ishwaran, Udaya B. Kogalur
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


Reference manual: randomForestSRC.pdf


Package source: randomForestSRC_3.3.0.tar.gz
Windows binaries: r-devel: randomForestSRC_3.3.0.zip, r-release: randomForestSRC_3.3.0.zip, r-oldrel: randomForestSRC_3.3.0.zip
macOS binaries: r-release (arm64): randomForestSRC_3.3.0.tgz, r-oldrel (arm64): randomForestSRC_3.3.0.tgz, r-release (x86_64): randomForestSRC_3.3.0.tgz, r-oldrel (x86_64): randomForestSRC_3.3.0.tgz
Old sources: randomForestSRC archive

Reverse dependencies:

Reverse depends: ggRandomForests
Reverse imports: AutoScore, boostmtree, cjbart, CoxAIPW, glmnetr, precmed, ranktreeEnsemble, SIMMS, survcompare, survivalSL, SurvMetrics, tehtuner
Reverse suggests: ClassifyR, familiar, LTRCforests, MachineShop, mlrCPO, multipleOutcomes, PheCAP, riskRegression, survex
Reverse enhances: pec


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