RegEnRF: Regression-Enhanced Random Forests
A novel generalized Random Forest method, that can improve on RFs by borrowing the strength of penalized parametric regression. Based on Zhang et al. (2019) <doi:10.48550/arXiv.1904.10416>.
| Version: | 1.0.0 |
| Imports: | glmnet, randomForest |
| Suggests: | testthat (≥ 3.0.0) |
| Published: | 2025-12-22 |
| DOI: | 10.32614/CRAN.package.RegEnRF (may not be active yet) |
| Author: | Umberto Minora 
[aut, cre, cph] |
| Maintainer: | Umberto Minora <umbertofilippo at tiscali.it> |
| BugReports: | https://github.com/umbe1987/regenrf/issues |
| License: | MIT + file LICENSE |
| URL: | https://github.com/umbe1987/regenrf |
| NeedsCompilation: | no |
| Citation: | RegEnRF citation info |
| Materials: | README, NEWS |
| CRAN checks: | RegEnRF results |
Documentation:
| Reference manual: | RegEnRF.html , RegEnRF.pdf |
Downloads:
| Package source: | RegEnRF_1.0.0.tar.gz |
| Windows binaries: | r-devel: not available, r-release: RegEnRF_1.0.0.zip, r-oldrel: not available |
| macOS binaries: | r-release (arm64): RegEnRF_1.0.0.tgz, r-oldrel (arm64): RegEnRF_1.0.0.tgz, r-release (x86_64): RegEnRF_1.0.0.tgz, r-oldrel (x86_64): RegEnRF_1.0.0.tgz |
Linking:
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