# Ranktration

Rank/compare algorithms, models, or approaches with weighted multi-criteria analysis.

## How It Works

1. **Collect trajectories** - Gather approaches with measurable metrics
2. **Smart sampling** - Select representative sample for large datasets
3. **Pairwise battles** - Compare sample trajectories using weighted scores
4. **Tournament ranking** - Establish global rankings through competitive analysis
5. **Statistical confidence** - Measure ranking stability and significance
6. **Final scoring** - Apply ranking bonuses to create comprehensive evaluation

## License

MIT License - see LICENSE file for details.

## Credit

This implementation is inspired by and derived from the [RULER (Robust Unified Learning Evaluation & Ranking)](https://art.openpipe.ai/fundamentals/ruler) framework originally developed by OpenPipe for AI evaluation and trajectory analysis in machine learning.

## Contact

For questions, issues, or contributions:

- GitHub: https://github.com/V-Sekai-fire/ranktration
- Hex.pm: https://hex.pm/packages/ranktration