Random Forests, Linear Trees, and Gradient Boosting for Inference and Interpretability R package Rforestry version 0.11.1.0 CRAN Task View: MissingData (https://CRAN.R-project.org/view=MissingData) Provides fast implementations of 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. Software Imports: Rcpp (>= 0.12.9), parallel, methods, visNetwork, glmnet (>= 4.1), grDevices, onehot LinkingTo: Rcpp, RcppArmadillo, RcppThread Suggests: testthat, knitr, rmarkdown, mvtnorm Theo Saarinen <theo_s@berkeley.edu> Comprehensive R Archive Network (CRAN) Sören Künzel [aut], Theo Saarinen [aut, cre], Simon Walter [aut], Sam Antonyan [aut], Edward Liu [aut], Allen Tang [aut], Jasjeet Sekhon [aut] GPL (>= 3) file LICENSE (https://CRAN.R-project.org/package=Rforestry/LICENSE) 2025-03-15 application/tgz https://CRAN.R-project.org/package=Rforestry doi:10.32614/CRAN.package.Rforestry