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, survival regression and treatment effect estimation (optionally using instrumental variables), with support for missing values.

Version: 1.2.0
Depends: R (≥ 3.5.0)
Imports: DiceKriging, lmtest, Matrix, methods, Rcpp (≥ 0.12.15), sandwich (≥ 2.4-0)
LinkingTo: Rcpp, RcppEigen
Suggests: DiagrammeR, testthat
Published: 2020-06-04
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, MissingData
CRAN checks: grf results

Downloads:

Reference manual: grf.pdf
Package source: grf_1.2.0.tar.gz
Windows binaries: r-devel: grf_1.2.0.zip, r-devel-UCRT: grf_1.2.0.zip, r-release: grf_1.2.0.zip, r-oldrel: grf_1.2.0.zip
macOS binaries: r-release (arm64): grf_1.2.0.tgz, r-release (x86_64): grf_1.2.0.tgz, r-oldrel: grf_1.2.0.tgz
Old sources: grf archive

Reverse dependencies:

Reverse imports: policytree, postDoubleR
Reverse suggests: uplifteval

Linking:

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