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) .


Reference manual

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install.packages("Rforestry") by Theo Saarinen, 5 months ago


Report a bug at https://github.com/forestry-labs/Rforestry/issues

Browse source code at https://github.com/cran/Rforestry

Authors: Sören Künzel [aut] , Theo Saarinen [aut, cre] , Simon Walter [aut] , Edward Liu [aut] , Allen Tang [aut] , Jasjeet Sekhon [aut]

Documentation:   PDF Manual  

GPL (>= 3) license

Imports Rcpp, parallel, methods, visNetwork, glmnet, grDevices, onehot, dplyr

Suggests testthat, knitr, rmarkdown, mvtnorm

Linking to Rcpp, RcppArmadillo, RcppThread

System requirements: C++11

See at CRAN