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: |
2.3.2 |
| Depends: |
R (≥ 3.5.0) |
| Imports: |
DiceKriging, lmtest, Matrix, methods, Rcpp (≥ 0.12.15), sandwich (≥ 2.4-0) |
| LinkingTo: |
Rcpp, RcppEigen |
| Suggests: |
DiagrammeR, MASS, rdd, survival (≥ 3.2-8), testthat (≥
3.0.4) |
| Published: |
2024-02-25 |
| 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 |