stats-confidence-intervals 0.2.3

A comprehensive library for calculating and visualizing statistical confidence intervals

Confidence Interval Library

A Python library for calculating and visualizing confidence intervals.

Installation

pip install confidence_interval

Features

• Calculate confidence intervals for means
• Calculate confidence intervals for proportions using:
  • Wilson score method (recommended for small sample sizes)
  • Normal approximation method (for large sample sizes)
• Visualize confidence intervals with customizable plots
• Support for both list and numpy array inputs
• Comprehensive error checking and validation

Usage

Mean Confidence Interval

from confidence_interval.core import mean_confidence_interval
import numpy as np

data = np.random.normal(100, 15, size=50)
ci = mean_confidence_interval(data, confidence=0.95)
print(f"Mean: {ci.estimate:.2f}")
print(f"95% CI: ({ci.lower_bound:.2f}, {ci.upper_bound:.2f})")

Proportion Confidence Interval

from confidence_interval.core import proportion_confidence_interval

successes = 45
total = 100
ci = proportion_confidence_interval(successes, total, confidence=0.95, method='wilson')
print(f"Proportion: {ci.estimate:.2f}")
print(f"95% CI: ({ci.lower_bound:.2f}, {ci.upper_bound:.2f})")

Development

This project uses GitHub Actions for automated testing and deployment. The workflow includes:

• Automated tests on Python versions 3.8-3.12
• Automatic releases when tags are pushed
• Automated PyPI publishing

Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Make your changes
4. Run tests (pytest tests/)
5. Commit your changes (git commit -m 'feat: Add amazing feature')
6. Push to the branch (git push origin feature/amazing-feature)
7. Open a Pull Request

Release Process

The release process is automated through GitHub Actions:

1. Update version in setup.py
2. Add entry to CHANGELOG.md
3. Commit changes
4. Create and push a tag:

   git tag -a v0.x.x -m "Release version 0.x.x"
   git push origin v0.x.x
   

5. GitHub Actions will automatically:
   • Create a GitHub release
   • Run tests
   • Publish to PyPI

License

This project is licensed under the MIT License - see the LICENSE file for details.

Changelog

See CHANGELOG.md for a list of changes and version history.