ocf: Ordered Correlation Forest

Machine learning estimator specifically optimized for predictive modeling of ordered non-numeric outcomes. 'ocf' provides forest-based estimation of the conditional choice probabilities and the covariates’ marginal effects. Under an "honesty" condition, the estimates are consistent and asymptotically normal and standard errors can be obtained by leveraging the weight-based representation of the random forest predictions. Please reference the use as Di Francesco (2025) <doi:10.1080/07474938.2024.2429596>.

Version:	1.0.3
Depends:	R (≥ 3.4.0)
Imports:	Rcpp, Matrix, stats, utils, stringr, orf, glmnet, ranger, dplyr, tidyr, ggplot2, magrittr
LinkingTo:	Rcpp, RcppEigen
Suggests:	knitr, rmarkdown, testthat (≥ 3.0.0)
Published:	2025-02-03
DOI:	10.32614/CRAN.package.ocf
Author:	Riccardo Di Francesco [aut, cre, cph]
Maintainer:	Riccardo Di Francesco <difrancesco.riccardo96 at gmail.com>
BugReports:	https://github.com/riccardo-df/ocf/issues
License:	GPL-3
URL:	https://riccardo-df.github.io/ocf/, https://github.com/riccardo-df/ocf
NeedsCompilation:	yes
Materials:	README, NEWS
CRAN checks:	ocf results

Documentation:

Reference manual:	ocf.html , ocf.pdf
Vignettes:	Short Tutorial (source, R code)

Downloads:

Package source:	ocf_1.0.3.tar.gz
Windows binaries:	r-devel: ocf_1.0.3.zip, r-release: ocf_1.0.3.zip, r-oldrel: ocf_1.0.3.zip
macOS binaries:	r-release (arm64): ocf_1.0.3.tgz, r-oldrel (arm64): ocf_1.0.3.tgz, r-release (x86_64): ocf_1.0.3.tgz, r-oldrel (x86_64): ocf_1.0.3.tgz
Old sources:	ocf archive

Reverse dependencies:

Reverse imports:

causalQual

Linking:

Please use the canonical form https://CRAN.R-project.org/package=ocf to link to this page.