Rforestry: Random Forests, Linear Trees, and Gradient Boosting for Inference and Interpretability

Provides fast implementations of Honest 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. Soren R. Kunzel, Theo F. Saarinen, Edward W. Liu, Jasjeet S. Sekhon (2019) <doi:10.48550/arXiv.1906.06463>.

Version:	0.10.0
Imports:	Rcpp (≥ 0.12.9), parallel, methods, visNetwork, glmnet (≥ 4.1), grDevices, onehot, pROC
LinkingTo:	Rcpp, RcppArmadillo, RcppThread
Suggests:	testthat, knitr, rmarkdown, mvtnorm
Published:	2023-03-25
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)
URL:	https://github.com/forestry-labs/Rforestry
NeedsCompilation:	yes
In views:	MissingData
CRAN checks:	Rforestry results

Documentation:

Reference manual:

Rforestry.pdf

Downloads:

Package source:	Rforestry_0.10.0.tar.gz
Windows binaries:	r-devel: Rforestry_0.10.0.zip, r-release: Rforestry_0.10.0.zip, r-oldrel: Rforestry_0.10.0.zip
macOS binaries:	r-release (arm64): Rforestry_0.10.0.tgz, r-oldrel (arm64): Rforestry_0.10.0.tgz, r-release (x86_64): Rforestry_0.10.0.tgz, r-oldrel (x86_64): Rforestry_0.10.0.tgz
Old sources:	Rforestry archive

Reverse dependencies:

Reverse imports:

distillML

Linking:

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