grf: Generalized Random Forests
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: |
2.2.0 |
Depends: |
R (≥ 3.5.0) |
Imports: |
DiceKriging, lmtest, Matrix, methods, Rcpp (≥ 0.12.15), sandwich (≥ 2.4-0) |
LinkingTo: |
Rcpp, RcppEigen |
Suggests: |
DiagrammeR, MASS, rdd, survival (≥ 3.2-8), testthat (≥
3.0.4) |
Published: |
2022-08-06 |
Author: |
Julie Tibshirani [aut, cre],
Susan Athey [aut],
Rina Friedberg [ctb],
Vitor Hadad [ctb],
David Hirshberg [ctb],
Luke Miner [ctb],
Erik Sverdrup [aut],
Stefan Wager [aut],
Marvin Wright [ctb] |
Maintainer: |
Julie Tibshirani <jtibs at cs.stanford.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 |
Documentation:
Downloads:
Reverse dependencies:
Linking:
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