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Contributing to RAP-validation

Thank you for your interest in contributing to the Recursive Attractor Principle validation framework! 🎯

Ways to Contribute

1. New Domain Validations

We're actively seeking validation of RAP across different domains:

  • Cancer growth dynamics (eukaryotic systems)
  • Market equilibria (economic systems)
  • Stellar evolution (cosmological systems)
  • Neural network training (artificial systems)
  • Any recursive system you think might show 85% convergence

2. Model Improvements

  • Alternative formulations of the RAP equations
  • Optimization of fitting algorithms
  • Better handling of edge cases
  • Performance improvements

3. Statistical Analysis

  • Robustness checks
  • Sensitivity analysis
  • Alternative statistical tests
  • Cross-validation methods

4. Documentation

  • Tutorials and examples
  • Code documentation improvements
  • Theoretical explanations
  • Use case demonstrations

How to Contribute

Reporting Issues

  • Use the GitHub issue tracker
  • Provide clear description of the problem
  • Include reproducible examples when possible
  • Specify your Python version and dependencies

Submitting Changes

  1. Fork the repository

    git clone https://github.com/shackled99/RAP-validation.git
cd RAP-validation

  2. Create a feature branch

    git checkout -b feature/your-feature-name

  3. Make your changes

    • Follow the existing code style
    • Add comments and docstrings
    • Update documentation if needed

  4. Test your changes

    • Ensure existing functionality still works
    • Add tests for new features

  5. Commit with clear messages

    git commit -m "Add: Brief description of changes"

  6. Push and create a Pull Request

    git push origin feature/your-feature-name

Code Style Guidelines

  • Follow PEP 8 for Python code
  • Use descriptive variable names
  • Add docstrings to all functions
  • Include type hints where helpful
  • Comment complex logic

Example:

def calculate_convergence(trajectory, target, tolerance=0.05):
    """
    Check if trajectory converged to target value.
    
    Parameters:
    -----------
    trajectory : array-like
        Time series data
    target : float
        Target convergence value
    tolerance : float
        Acceptable deviation from target
    
    Returns:
    --------
    bool
        True if converged within tolerance
    """
    final_value = trajectory[-1]
    return abs(final_value - target) < tolerance

Domain Validation Guidelines

If you're validating RAP in a new domain, please include:

  1. Clear data source - Provide link/citation to dataset
  2. Preprocessing steps - Document any data cleaning
  3. Fitting results - Report convergence statistics
  4. Comparison baseline - Compare to domain-specific models
  5. Visualization - Create plots showing convergence
  6. Statistical analysis - Sample size, significance tests

Validation Checklist

  • Dataset description and source
  • Data preprocessing code
  • RAP model fitting code
  • Baseline model comparison
  • Convergence analysis (% at 85%)
  • Statistical significance tests
  • Visualization plots
  • README documentation

Questions?

  • Open an issue for discussion
  • Tag with question label
  • Be respectful and constructive

Attribution

Contributors will be acknowledged in:

  • README acknowledgments section
  • Git commit history
  • Publications (for significant contributions)

License

By contributing, you agree that your contributions will be licensed under the same MIT License that covers this project.

Thank you for helping advance our understanding of recursive attractor dynamics! 🥔