grf: Generalized Random Forests

A pluggable package for forest-based statistical estimation and inference. GRF currently provides methods for non-parametric least-squares regression, quantile regression, and treatment effect estimation (optionally using instrumental variables).

Version: 0.10.4
Depends: R (≥ 3.3.0)
Imports: DiceKriging, lmtest, Matrix, methods, Rcpp (≥ 0.12.15), sandwich (≥ 2.4-0)
LinkingTo: Rcpp, RcppEigen
Suggests: DiagrammeR, testthat
Published: 2019-09-03
Author: Julie Tibshirani [aut, cre], Susan Athey [aut], Rina Friedberg [ctb], Vitor Hadad [ctb], David Hirshberg [ctb], Luke Miner [ctb], Erik Sverdrup [ctb], 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: MachineLearning
CRAN checks: grf results

Downloads:

Reference manual: grf.pdf
Package source: grf_0.10.4.tar.gz
Windows binaries: r-devel: grf_0.10.4.zip, r-devel-gcc8: grf_0.10.4.zip, r-release: grf_0.10.4.zip, r-oldrel: grf_0.10.4.zip
OS X binaries: r-release: grf_0.10.4.tgz, r-oldrel: grf_0.10.4.tgz
Old sources: grf archive

Reverse dependencies:

Reverse imports: postDoubleR
Reverse suggests: StratifiedMedicine, uplifteval

Linking:

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