orf: Ordered Random Forests
An implementation of the Ordered Forest estimator as developed
in Lechner & Okasa (2019) <doi:10.48550/arXiv.1907.02436>. The Ordered Forest flexibly
estimates the conditional probabilities of models with ordered categorical
outcomes (so-called ordered choice models). Additionally to common machine
learning algorithms the 'orf' package provides functions for estimating
marginal effects as well as statistical inference thereof and thus provides
similar output as in standard econometric models for ordered choice. The
core forest algorithm relies on the fast C++ forest implementation from
the 'ranger' package (Wright & Ziegler, 2017) <doi:10.48550/arXiv.1508.04409>.
| Version: |
0.1.4 |
| Depends: |
R (≥ 2.10) |
| Imports: |
ggplot2, ranger, Rcpp, stats, utils, xtable |
| LinkingTo: |
Rcpp |
| Suggests: |
knitr, rmarkdown, testthat |
| Published: |
2022-07-23 |
| DOI: |
10.32614/CRAN.package.orf |
| Author: |
Gabriel Okasa [aut, cre], Michael Lechner [ctb] |
| Maintainer: |
Gabriel Okasa <okasa.gabriel at gmail.com> |
| BugReports: |
https://github.com/okasag/orf/issues |
| License: |
GPL-3 |
| URL: |
https://github.com/okasag/orf |
| NeedsCompilation: |
yes |
| Citation: |
orf citation info |
| Materials: |
README, NEWS |
| CRAN checks: |
orf results |
Documentation:
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