Package: ocf 1.0.3
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>.
Authors:
ocf_1.0.3.tar.gz
ocf_1.0.3.zip(r-4.7)ocf_1.0.3.zip(r-4.6)ocf_1.0.3.zip(r-4.5)
ocf_1.0.3.tgz(r-4.6-x86_64)ocf_1.0.3.tgz(r-4.6-arm64)ocf_1.0.3.tgz(r-4.5-x86_64)ocf_1.0.3.tgz(r-4.5-arm64)
ocf_1.0.3.tar.gz(r-4.7-arm64)ocf_1.0.3.tar.gz(r-4.7-x86_64)ocf_1.0.3.tar.gz(r-4.6-arm64)ocf_1.0.3.tar.gz(r-4.6-x86_64)
ocf_1.0.3.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
ocf/json (API)
NEWS
| # Install 'ocf' in R: |
| install.packages('ocf', repos = c('https://riccardo-df.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/riccardo-df/ocf/issues
Pkgdown/docs site:https://riccardo-df.github.io
Last updated from:5de2d60cd0. Checks:11 WARNING, 1 ERROR, 1 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-arm64 | WARNING | 201 | ||
| linux-devel-x86_64 | WARNING | 193 | ||
| source / vignettes | ERROR | 264 | ||
| linux-release-arm64 | WARNING | 227 | ||
| linux-release-x86_64 | WARNING | 201 | ||
| macos-release-arm64 | WARNING | 261 | ||
| macos-release-x86_64 | WARNING | 345 | ||
| macos-oldrel-arm64 | WARNING | 178 | ||
| macos-oldrel-x86_64 | WARNING | 323 | ||
| windows-devel | WARNING | 191 | ||
| windows-release | WARNING | 169 | ||
| windows-oldrel | WARNING | 183 | ||
| wasm-release | OK | 147 |
Exports:classification_errorgenerate_ordered_datamarginal_effectsmean_absolute_errormean_ranked_scoremean_squared_errormultinomial_mlocfordered_mltree_info
Dependencies:clicodetoolscpp11dplyrfarverforeachgenericsggplot2ggthemesglmnetgluegtableisobanditeratorslabelinglatticelifecyclemagrittrMatrixorfpillarpkgconfigpurrrR6rangerRColorBrewerRcppRcppEigenrlangS7scalesshapestringistringrsurvivaltibbletidyrtidyselectutf8vctrsviridisLitewithrxtable
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Generate Ordered Data | generate_ordered_data |
| Marginal Effects for Ordered Correlation Forest | marginal_effects |
| Accuracy Measures for Ordered Probability Predictions | classification_error mean_absolute_error mean_ranked_score mean_squared_error |
| Multinomial Machine Learning | multinomial_ml |
| Ordered Correlation Forest | ocf |
| Ordered Machine Learning | ordered_ml |
| Plot Method for ocf.marginal Objects | plot.ocf.marginal |
| Prediction Method for mml Objects | predict.mml |
| Prediction Method for ocf Objects | predict.ocf |
| Prediction Method for oml Objects | predict.oml |
| Print Method for ocf Objects | print.ocf |
| Print Method for ocf.marginal Objects | print.ocf.marginal |
| Summary Method for ocf Objects | summary.ocf |
| Summary Method for ocf.marginal Objects | summary.ocf.marginal |
| Tree Information in Readable Format | tree_info |
