Installing Ravix

Python 3.9+MIT License

Ravix is available on PyPI and requires Python 3.9 or later.

pip

The recommended installation method:

pip install ravix

To upgrade to the latest version:

pip install --upgrade ravix

From source

git clone https://github.com/danielmcgibney/ravix
cd ravix
pip install -e .

Verify installation

# Should print the installed version
import ravix
print(ravix.__version__)

Dependencies

All required dependencies are installed automatically by pip.

Quickstart

import ravix

df    = ravix.get_data("Betas.csv")
model = ravix.ols("AAPL ~ SPY", df)
model.summary()
ravix.plot("AAPL ~ SPY", df)

About Us

Ravix is a unified statistical modeling and visualization framework for Python designed to support applied regression analysis in business analytics. Developed from years of teaching regression at the graduate level, Ravix emphasizes clarity, interpretation, and workflow consistency over low-level coding complexity. By integrating formula-based modeling, streamlined output, diagnostics, and visualization into a cohesive interface, Ravix allows students and practitioners to focus on statistical relationships and communicating insights rather than managing implementation details. Ravix serves as the companion framework for Applied Linear Regression for Business Analytics with Python: A Practical Guide Using Ravix with Case Studies (Springer, 2026).

Today, Ravix supports graduate and undergraduate analytics education as well as applied business modeling workflows for practitioners seeking clear, reproducible regression analysis in Python.

Contributors

Ravix was developed with the support of undergraduate and graduate students in the Management Science Department at the University of Miami.

Nefeli Zafeiri contributed to the development of Ravix and was instrumental in the design of this website. Mintra Putlek and Kaitlyn Salvi Cruz contributed to the development and testing of Ravix.

Textbook

Applied Linear Regression for Business Analytics with Python

A Practical Guide Using Ravix with Case Studies

Daniel P. McGibney — Springer, 2026

Tailored for graduate students in MSBA and MBA programs and suitable for advanced undergraduates in analytics, math, and statistics. Every chapter uses Ravix throughout.

Springer → Amazon → businessregression.com ↗

Author

Daniel P. McGibney is the author of Applied Linear Regression for Business Analytics (Springer) and the core developer of Ravix. His teaching philosophy motivated Ravix's design: code should reinforce statistical concepts, not obscure them.

Contact

• Email: dmcgibney@bus.miami.edu
• GitHub: github.com/danielmcgibney/ravix
• Textbook site: businessregression.com
• PyPI: pypi.org/project/ravix

Community

Ravix is a focused package built around a single textbook. Here are the best places to get help and connect with other users.

GitHub

• Source code: github.com/danielmcgibney/ravix
• Bug reports and feature requests: Issues
• Discussions: GitHub Discussions

When reporting a bug, please include:

1. Your Python version: python --version
2. Your Ravix version: import ravix; print(ravix.__version__)
3. A minimal reproducible example
4. The full error traceback

Textbook resources

The companion site businessregression.com contains datasets and topic summaries for all textbook chapters.

Stack Overflow

Tag questions with ravix and python on Stack Overflow.

Code of conduct

All Ravix community spaces follow a simple standard: be respectful, be constructive, and assume good faith. The full code of conduct is in the repository.

Contributing to Ravix

Ravix is openly developed on GitHub. Contributions of all kinds are welcome — code, tests, documentation, and bug reports.

Dev environment

# Clone your fork
git clone https://github.com/danielmcgibney/ravix
cd ravix

# Install with dev dependencies
pip install -e ".[dev]"

# Run the test suite
pytest tests/ -v

Code standards

• Follow PEP 8 — run flake8 ravix/ before submitting
• Auto-format with black ravix/
• All public functions need a NumPy-style docstring
• New features require unit tests in tests/

Pull request checklist

Before you open a pull request, run through this short list. It helps the maintainer review your change quickly.

• ☐ All tests pass. Run pytest tests/ -v in your terminal and confirm everything is green.
• ☐ You added a test for your change. If you wrote a new function or fixed a bug, add at least one test that covers it.
• ☐ Every new function has a docstring. A short description in triple quotes explaining what the function does, its inputs, and what it returns.
• ☐ You noted the change in CHANGELOG.md. Add a one-line summary under the "Unreleased" heading so it appears in the next release notes.
• ☐ Your PR description explains what and why. A few sentences telling the maintainer what you changed and the reason behind it.