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: | 1.1.0 |
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: | 2020-03-12 |
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 |
Reference manual: | grf.pdf |
Package source: | grf_1.1.0.tar.gz |
Windows binaries: | r-prerelease: grf_1.1.0.zip, r-release: grf_1.1.0.zip, r-oldrel: grf_1.1.0.zip |
macOS binaries: | r-prerelease: grf_1.1.0.tgz, r-release: grf_1.1.0.tgz, r-oldrel: grf_1.1.0.tgz |
Old sources: | grf archive |
Reverse imports: | policytree, postDoubleR |
Reverse suggests: | StratifiedMedicine, uplifteval |
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