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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: | 3.3.1 |
| Depends: | R (≥ 4.3.0) |
| Imports: | parallel, data.tree, DiagrammeR |
| Suggests: | survival, pec, prodlim, mlbench, interp, caret, imbalance, cluster, fst, data.table |
| Published: | 2024-07-25 |
| 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_3.3.1.tar.gz |
| Windows binaries: | r-devel: randomForestSRC_3.3.1.zip, r-release: randomForestSRC_3.3.1.zip, r-oldrel: randomForestSRC_3.3.1.zip |
| macOS binaries: | r-release (arm64): randomForestSRC_3.3.1.tgz, r-oldrel (arm64): randomForestSRC_3.3.1.tgz, r-release (x86_64): randomForestSRC_3.3.1.tgz, r-oldrel (x86_64): randomForestSRC_3.3.1.tgz |
| Old sources: | randomForestSRC archive |
| 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, tram |
| Reverse enhances: | pec |
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.