# 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.
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