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 (2023)
<doi:10.48550/arXiv.2309.08755>.
| Version: |
1.0.1 |
| Depends: |
R (≥ 3.4.0) |
| Imports: |
Rcpp, Matrix, stats, utils, stringr, orf, glmnet, ranger |
| LinkingTo: |
Rcpp, RcppEigen |
| Suggests: |
knitr, rmarkdown, testthat (≥ 3.0.0) |
| Published: |
2024-09-25 |
| 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:
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