Science Score: 13.0%
This score indicates how likely this project is to be science-related based on various indicators:
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○CITATION.cff file
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✓codemeta.json file
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○.zenodo.json file
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○DOI references
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○Academic publication links
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○Institutional organization owner
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○Scientific vocabulary similarity
Low similarity (18.3%) to scientific vocabulary
Repository
Basic Info
- Host: GitHub
- Owner: riccardo-df
- License: gpl-3.0
- Language: C++
- Default Branch: main
- Homepage: https://riccardo-df.github.io/ocf/
- Size: 7.13 MB
Statistics
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
- Releases: 0
Metadata Files
README.md
Ordered correlation forest 
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.
Why use ocf?
| 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. |
🚀 Installation
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.
Contributing
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: 1. Fork the repository and create a new branch. 2. Ensure that your code follows the existing style and documentation conventions. 3. Run tests and check for package integrity. 4. 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.
Citation
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.
Owner
- Name: Riccardo Di Francesco
- Login: riccardo-df
- Kind: user
- Website: https://riccardo-df.github.io/
- Repositories: 3
- Profile: https://github.com/riccardo-df
Ph.D. candidate at University of Rome Tor Vergata.
GitHub Events
Total
- Push event: 50
Last Year
- Push event: 50
Packages
- Total packages: 1
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Total downloads:
- cran 189 last-month
- Total dependent packages: 0
- Total dependent repositories: 0
- Total versions: 3
- Total maintainers: 1
cran.r-project.org: ocf
Ordered Correlation Forest
- Homepage: https://riccardo-df.github.io/ocf/
- Documentation: http://cran.r-project.org/web/packages/ocf/ocf.pdf
- License: GPL-3
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Latest release: 1.0.3
published over 1 year ago