ocf implements the ordered correlation forest estimator, a machine learning estimator specifically designed for predictive modeling of ordered non-numeric outcomes.
The package delivers:
✔ Forest-based estimation of conditional choice probabilities.
✔ Marginal effects of covariates on the choice probabilities.
✔ Standard error estimation leveraging the weight-based structure of random forest predictions.
| Feature | Benefit |
|---|---|
| Optimized for ordered outcomes | Unlike traditional machine learning models, ocf correctly handles ordered categorical data. |
| Interpretable marginal effects | Understand how covariates correlate with choice probabilities. |
| Easy to use | Integrates seamlessly into existing machine learning workflows. |
| Active development & support | Open-source and actively maintained. |
To install the latest stable version from CRAN:
install.packages("ocf")
Alternatively, the current development version of the package can be installed using the devtools package:
devtools::install_github("riccardo-df/ocf") # run install.packages("devtools") if needed.
We welcome contributions! If you encounter issues, have feature requests, or want to contribute to the package, please follow the guidelines below.
📌 Report an issue: If you encounter a bug or have a suggestion, please open an issue on GitHub: Submit an issue
📌 Contribute code: We encourage contributions via pull requests. Before submitting, please:
- Fork the repository and create a new branch.
- Ensure that your code follows the existing style and documentation conventions.
- Run tests and check for package integrity.
- Submit a pull request with a clear description of your changes.
📌 Feature requests: If you have ideas for new features or extensions, feel free to discuss them by opening an issue.
If you use ocf in your research, please cite the corresponding paper:
Di Francesco, R. (2025). Ordered Correlation Forest. Econometric Reviews 44(4), 416-432.