grf

Generalized Random Forests (Beta)

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). This package is currently in beta, and we expect to make continual improvements to its performance and usability.

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Description file content

Package
grf
Title
Generalized Random Forests (Beta)
Version
0.10.0
Author
Julie Tibshirani [aut, cre], Susan Athey [aut], Stefan Wager [aut], Rina Friedberg [ctb], Luke Miner [ctb], Marvin Wright [ctb]
BugReports
https://github.com/swager/grf/issues
Maintainer
Julie Tibshirani
Description
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). This package is currently in beta, and we expect to make continual improvements to its performance and usability.
Depends
R (>= 3.3.0)
License
GPL-3
LinkingTo
Rcpp, RcppEigen
Imports
DiceKriging, Matrix, methods, Rcpp (>= 0.12.15), sandwich (>= 2.4-0)
RoxygenNote
6.0.1.9000
Suggests
testthat
SystemRequirements
GNU make
URL
NeedsCompilation
yes
Packaged
2018-05-09 07:00:52 UTC; jtibshirani
Repository
CRAN
Date/Publication
2018-05-09 09:05:35 UTC

install.packages('grf')

0.10.0

2 months ago

https://github.com/swager/grf

Julie Tibshirani

GPL-3

Depends on

R (>= 3.3.0)

Imports

DiceKriging, Matrix, methods, Rcpp (>= 0.12.15), sandwich (>= 2.4-0)

Suggests

testthat

Discussions