https://github.com/dmavani25/discopula
Discrete checkerboard copula modeling and implementation of new scoring methods pertaining to ordinal and categorical discrete data.
https://github.com/dmavani25/discopula
checkerboard-copula copula-models discrete-modeling regression-algorithms statistics
Last synced: 5 months ago
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Discrete checkerboard copula modeling and implementation of new scoring methods pertaining to ordinal and categorical discrete data.
- Host: GitHub
- URL: https://github.com/dmavani25/discopula
- Owner: dmavani25
- License: mit
- Created: 2024-11-07T23:20:52.000Z (11 months ago)
- Default Branch: master
- Last Pushed: 2025-05-03T05:21:59.000Z (6 months ago)
- Last Synced: 2025-05-03T06:22:09.639Z (6 months ago)
- Topics: checkerboard-copula, copula-models, discrete-modeling, regression-algorithms, statistics
- Language: Python
- Homepage: https://pypi.org/project/discopula/
- Size: 3.33 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: .github/README.md
- Contributing: docs/contributing.md
- License: LICENSE.txt
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README
# discopula
> Discrete checkerboard copula modeling and implementation of new scoring methods pertaining to ordinal and categorical discrete data.
[](https://badge.fury.io/py/discopula)
[](https://github.com/dmavani25/discopula/actions/workflows/test.yaml)
[](https://discopula.readthedocs.io/en/latest/?badge=latest)
[](https://coveralls.io/github/dmavani25/discopula?branch=master)
[](https://github.com/christophevg/pypi-template)
## Installation
This package (discopula) is hosted on PyPi, so for installation follow the following workflow ...
```console
$ pip install discopula
```
Now, you should be all set to use it in a Jupyter Notebook!
Alternatively, if you would like to use it in a project, we recommend you to have a virtual environment for your use of this package, then follow the following workflow. For best practices, it's recommended to use a virtual environment:
1. First, create and activate a virtual environment (Python 3.8+ recommended):
```bash
# Create virtual environment
$ python -m venv discopula-env
# Activate virtual environment (Mac/Linux)
$ source discopula-env/bin/activate
# Verify you're in the virtual environment
$ which python
```
2. Install package
```bash
$ pip install discopula
```
3. To deactivate the virtual environment, when done:
```bash
$ deactivate
```
## Documentation
Visit [Read the Docs](https://discopula.readthedocs.org) for the full documentation, including overviews and several examples.
## Examples
For detailed examples in Jupyter Notebooks and beyond (organized by functionality) please refer to our [GitHub repository's examples folder](https://github.com/dmavani25/discopula/tree/master/examples).
## Features
- Construction of checkerboard copulas from contingency tables and/or list of cases
- Calculation of marginal distributions and CDFs
- Computation of Checkerboard Copula Regression (CCR) and Prediction based on CCR
- Implementation of Checkerboard Copula Regression Association Measure (CCRAM) and the Scaled CCRAM (SCCRAM)
- Bootstrap functionality for CCR-based prediction, CCRAM and SCCRAM
- Permutation testing functionality for CCRAM & SCCRAM
- Vectorized implementations for improved performance
- Rigorous Edge-case Handling & Unit Testing with Pytest
## Contributing
Contributions are welcome! Please feel free to submit a Pull Request.
## License
This project is licensed under the MIT License - see the LICENSE file for details.