Generalized Random Forests [R package grf version 2.5.0]

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: 2.5.0

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年10月09日

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

Documentation:

Reference manual: grf.html , grf.pdf

Downloads:

Package source: grf_2.5.0.tar.gz

Windows binaries: r-devel: grf_2.5.0.zip, r-release: grf_2.5.0.zip, r-oldrel: grf_2.5.0.zip

macOS binaries: r-release (arm64): grf_2.5.0.tgz, r-oldrel (arm64): grf_2.5.0.tgz, r-release (x86_64): grf_2.5.0.tgz, r-oldrel (x86_64): grf_2.5.0.tgz

Old sources: grf archive

Reverse dependencies:

Reverse imports: aggTrees, causalDT, causalQual, causalweight, cohetsurr, cramR, EpiForsk, evalITR, htetree, longsurr, OutcomeWeights, policytree, qeML, roseRF

Reverse suggests: CRE, maq, rdss, targeted

Linking:

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