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


Reference manual: orf.pdf
Vignettes: orf: ordered random forests


Package source: orf_0.1.4.tar.gz
Windows binaries: r-devel: orf_0.1.4.zip, r-release: orf_0.1.4.zip, r-oldrel: orf_0.1.4.zip
macOS binaries: r-release (arm64): orf_0.1.4.tgz, r-oldrel (arm64): orf_0.1.4.tgz, r-release (x86_64): orf_0.1.4.tgz, r-oldrel (x86_64): orf_0.1.4.tgz
Old sources: orf archive

Reverse dependencies:

Reverse imports: ocf


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