Rforestry: Random Forests, Linear Trees, and Gradient Boosting forInference and Interpretability
Provides fast implementations of Random Forests, Gradient Boosting, and Linear Random Forests, with an emphasis on inference and interpretability. Additionally contains methods for variable importance, out-of-bag prediction, regression monotonicity, and several methods for missing data imputation.
| Version: | 0.11.1.0 |
| Imports: | Rcpp (≥ 0.12.9), parallel, methods,visNetwork,glmnet (≥4.1), grDevices,onehot |
| LinkingTo: | Rcpp,RcppArmadillo,RcppThread |
| Suggests: | testthat,knitr,rmarkdown,mvtnorm |
| Published: | 2025-03-15 |
| DOI: | 10.32614/CRAN.package.Rforestry |
| Author: | Sören Künzel [aut], Theo Saarinen [aut, cre], Simon Walter [aut], Sam Antonyan [aut], Edward Liu [aut], Allen Tang [aut], Jasjeet Sekhon [aut] |
| Maintainer: | Theo Saarinen <theo_s at berkeley.edu> |
| BugReports: | https://github.com/forestry-labs/Rforestry/issues |
| License: | GPL (≥ 3) | fileLICENSE |
| URL: | https://github.com/forestry-labs/Rforestry |
| NeedsCompilation: | yes |
| Materials: | README |
| In views: | MissingData |
| CRAN checks: | Rforestry results |
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