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: 3.4.1 Depends: R (≥ 4.3.0) Imports: parallel, data.tree, DiagrammeR Suggests: survival, pec, prodlim, mlbench, interp, caret, imbalance, cluster, fst, data.table Published: 2025-06-08 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: Downloads: Reverse dependencies: Reverse imports: cjbart, CoxAIPW, glmnetr, precmed, ranktreeEnsemble, SIMMS, survcompare, survivalSL, tehtuner Reverse suggests: ClassifyR, familiar, MachineShop, mlrCPO, multipleOutcomes, PheCAP, riskRegression, survex, SurvMetrics, tram Reverse enhances: pec Linking:Please use the canonical form https://CRAN.R-project.org/package=randomForestSRC to link to this page.
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