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