Forest-based statistical estimation and inference. GRF provides non-parametric methods for heterogeneous treatment effects estimation (optionally using right-censored outcomes, multiple treatment arms or outcomes, or instrumental variables), as well as least-squares regression, quantile regression, and survival regression, all with support for missing covariates.
| Version: | 2.5.0 |
| Depends: | R (≥ 3.5.0) |
| Imports: | DiceKriging,lmtest,Matrix, methods,Rcpp (≥ 0.12.15),sandwich (≥ 2.4-0) |
| LinkingTo: | Rcpp,RcppEigen |
| Suggests: | DiagrammeR,MASS,rdrobust,survival (≥ 3.2-8),testthat (≥3.0.4) |
| Published: | 2025-10-09 |
| DOI: | 10.32614/CRAN.package.grf |
| Author: | Julie Tibshirani [aut], Susan Athey [aut], Rina Friedberg [ctb], Vitor Hadad [ctb], David Hirshberg [ctb], Luke Miner [ctb], Erik Sverdrup [aut, cre], Stefan Wager [aut], Marvin Wright [ctb] |
| Maintainer: | Erik Sverdrup <erik.sverdrup at monash.edu> |
| BugReports: | https://github.com/grf-labs/grf/issues |
| License: | GPL-3 |
| URL: | https://github.com/grf-labs/grf |
| NeedsCompilation: | yes |
| SystemRequirements: | GNU make |
| In views: | CausalInference,Econometrics,MachineLearning,MissingData |
| CRAN checks: | grf results |