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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
Author: Umberto Minora ORCID iD [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: RegEnRF_1.0.0.zip, r-release: RegEnRF_1.0.0.zip, r-oldrel: RegEnRF_1.0.0.zip
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:

Please use the canonical form https://CRAN.R-project.org/package=RegEnRF to link to this page.

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.
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