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
- 0.9.6
- 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
- 2018-04-14 08:29:16 UTC; jtibshirani
- Repository
- CRAN
- Date/Publication
- 2018-04-14 14:50:00 UTC
install.packages('grf')
0.9.6
5 days ago
Julie Tibshirani
GPL-3
Depends on
R (>= 3.3.0)
Imports
DiceKriging, Matrix, methods, Rcpp (>= 0.12.15), sandwich (>= 2.4-0)