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 |
| DOI: |
10.32614/CRAN.package.Rforestry |
| 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:
Downloads:
Reverse dependencies:
Linking:
Please use the canonical form
https://CRAN.R-project.org/package=Rforestry
to link to this page.