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) <arXiv:1906.06463>.

Imports: Rcpp (≥ 0.12.9), parallel, methods, visNetwork, glmnet, grDevices, onehot
LinkingTo: Rcpp, RcppArmadillo, RcppThread
Suggests: testthat, knitr, rmarkdown, mvtnorm
Published: 2021-04-02
Author: Sören Künzel [aut], Theo Saarinen [aut, cre], Simon Walter [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
SystemRequirements: C++11
Materials: README
CRAN checks: Rforestry results


Reference manual: Rforestry.pdf
Package source: Rforestry_0.9.0.4.tar.gz
Windows binaries: r-devel: Rforestry_0.9.0.4.zip, r-release: Rforestry_0.9.0.4.zip, r-oldrel: Rforestry_0.9.0.4.zip
macOS binaries: r-release: Rforestry_0.9.0.4.tgz, r-oldrel: Rforestry_0.9.0.4.tgz


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