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: 2.3.2
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>
License: GPL-3
NeedsCompilation: yes
SystemRequirements: GNU make
In views: CausalInference, Econometrics, MachineLearning, MissingData
CRAN checks: grf results


Reference manual: grf.pdf


Package source: grf_2.3.2.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): grf_2.3.2.tgz, r-oldrel (arm64): grf_2.3.2.tgz, r-release (x86_64): grf_2.3.2.tgz, r-oldrel (x86_64): grf_2.3.2.tgz
Old sources: grf archive

Reverse dependencies:

Reverse imports: aggTrees, causalweight, EpiForsk, evalITR, htetree, longsurr, policytree, qeML
Reverse suggests: CRE, maq, targeted


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