# ccrvam
> Python implementation of Checkerboard Copula Regression-based Visualization and Association Measure (CCRVAM)
[](https://badge.fury.io/py/ccrvam)
[](https://github.com/DhyeyMavani2003/ccrvam/actions/workflows/test.yaml)
[](https://ccrvam.readthedocs.io/en/latest/?badge=latest)
[](https://coveralls.io/github/DhyeyMavani2003/ccrvam?branch=main)
[](https://github.com/christophevg/pypi-template)
## Installation
This package (ccrvam) is hosted on PyPi, so for installation add the following line at the top of your Jupyter notebook!
```python
%pip install ccrvam
```
**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 ccrvam-env
# Activate virtual environment (Mac/Linux)
$ source ccrvam-env/bin/activate
# Verify you're in the virtual environment
$ which python
```
2. Install package
```bash
$ pip install ccrvam
```
3. To deactivate the virtual environment, when done:
```bash
$ deactivate
```
## Documentation
Visit [Read the Docs](https://ccrvam.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/DhyeyMavani2003/ccrvam/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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"description": "# ccrvam\n\n> Python implementation of Checkerboard Copula Regression-based Visualization and Association Measure (CCRVAM)\n\n[](https://badge.fury.io/py/ccrvam)\n[](https://github.com/DhyeyMavani2003/ccrvam/actions/workflows/test.yaml)\n[](https://ccrvam.readthedocs.io/en/latest/?badge=latest)\n[](https://coveralls.io/github/DhyeyMavani2003/ccrvam?branch=main)\n[](https://github.com/christophevg/pypi-template)\n\n## Installation\n\nThis package (ccrvam) is hosted on PyPi, so for installation add the following line at the top of your Jupyter notebook!\n\n```python\n%pip install ccrvam\n```\n\n**Now, you should be all set to use it in a Jupyter Notebook!**\n\nAlternatively, 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:\n\n1. First, create and activate a virtual environment (Python 3.8+ recommended):\n\n```bash\n# Create virtual environment\n$ python -m venv ccrvam-env\n\n# Activate virtual environment (Mac/Linux)\n$ source ccrvam-env/bin/activate\n\n# Verify you're in the virtual environment\n$ which python\n```\n\n2. Install package\n\n```bash\n$ pip install ccrvam\n```\n\n3. To deactivate the virtual environment, when done:\n\n```bash\n$ deactivate\n```\n\n## Documentation\n\nVisit [Read the Docs](https://ccrvam.readthedocs.org) for the full documentation, including overviews and several examples.\n\n## Examples\n\nFor detailed examples in Jupyter Notebooks and beyond (organized by functionality) please refer to our [GitHub repository's examples folder](https://github.com/DhyeyMavani2003/ccrvam/tree/master/examples).\n\n## Features\n\n- Construction of checkerboard copulas from contingency tables and/or list of cases\n- Calculation of marginal distributions and CDFs\n- Computation of Checkerboard Copula Regression (CCR) and Prediction based on CCR\n- Implementation of Checkerboard Copula Regression Association Measure (CCRAM) and the Scaled CCRAM (SCCRAM)\n- Bootstrap functionality for CCR-based prediction, CCRAM and SCCRAM\n- Permutation testing functionality for CCRAM & SCCRAM\n- Vectorized implementations for improved performance\n- Rigorous Edge-case Handling & Unit Testing with Pytest \n\n## Contributing\n\nContributions are welcome! Please feel free to submit a Pull Request.\n\n## License\n\nThis project is licensed under the MIT License - see the LICENSE file for details.\n\n\n",
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