A pluggable package for forest-based statistical estimation and inference. GRF currently provides methods for non-parametric least-squares regression, quantile regression, and treatment effect estimation (optionally using instrumental variables). This package is currently in beta, and we expect to make continual improvements to its performance and usability.
| Version: | 0.9.6 |
| Depends: | R (≥ 3.3.0) |
| Imports: | DiceKriging, Matrix, methods, Rcpp (≥ 0.12.15), sandwich (≥ 2.4-0) |
| LinkingTo: | Rcpp, RcppEigen |
| Suggests: | testthat |
| Published: | 2018-04-14 |
| Author: | Julie Tibshirani [aut, cre], Susan Athey [aut], Stefan Wager [aut], Rina Friedberg [ctb], Luke Miner [ctb], Marvin Wright [ctb] |
| Maintainer: | Julie Tibshirani <jtibs at cs.stanford.edu> |
| BugReports: | https://github.com/swager/grf/issues |
| License: | GPL-3 |
| URL: | https://github.com/swager/grf |
| NeedsCompilation: | yes |
| SystemRequirements: | GNU make |
| In views: | MachineLearning |
| CRAN checks: | grf results |
| Reference manual: | grf.pdf |
| Package source: | grf_0.9.6.tar.gz |
| Windows binaries: | r-devel: grf_0.9.6.zip, r-release: grf_0.9.6.zip, r-oldrel: grf_0.9.6.zip |
| OS X binaries: | r-release: grf_0.9.6.tgz, r-oldrel: grf_0.9.6.tgz |
| Old sources: | grf archive |
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