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>.

Getting started

Package details

AuthorSören Künzel [aut], Theo Saarinen [aut, cre], Simon Walter [aut], Sam Antonyan [aut], Edward Liu [aut], Allen Tang [aut], Jasjeet Sekhon [aut]
MaintainerTheo Saarinen <theo_s@berkeley.edu>
LicenseGPL (>= 3)
URL https://github.com/forestry-labs/Rforestry
Package repositoryView on CRAN
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Rforestry documentation built on March 31, 2023, 11:33 p.m.