# Digamma Prime (`digamma-ep`)

Sistema simbólico para auditoria de modelos com métricas de divergência estrutural, temporal e algébrica.

Symbolic audit framework for comparing models, tracking divergence, and teaching algebraic structure.

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## 📦 Instalação / Installation

```bash
pip install digamma-ep


#Basic Usage

from epe_maria import phi, delta_phi, phi_star

f = lambda x: x**2 + 2*x + 1
g = lambda x: x**2 + x + 1

print(phi(f, g))        # Divergência estrutural
print(delta_phi(f, g))  # Divergência de taxa
print(phi_star(f, g))   # Métrica de fusão

#Tests

pytest test_benchmark.py
pytest test_monitor.py

#Documentation
See examples and explanations in docs/ep_documentacao

#About
Created by Cerene Rúbio
License: MIT


# 🔮 Digamma Prime — Symbolic Model Auditing Framework

Digamma Prime is a symbolic framework for comparing machine learning models by their **behavior**, not just their outputs.

It introduces interpretable metrics like:

- **φ** — structural divergence  
- **Δφ** — rate divergence  
- **φ*** — fusion metric  
- **drift** — directional bias  
- **curvature** — shape sensitivity

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## 🚀 Features

- Symbolic comparison of models  
- CLI for drift detection and audits  
- Visual modules for φ and Δφ  
- Tutorials and case studies  
- Ready for CI/CD and dashboards

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## 📚 Documentation

See the full docs in the `docs/` folder or at [GitHub Pages link once published].

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## 🧪 Quickstart

```python
from epe_maria import phi, delta_phi
phi_val = phi(model_a.predict(X), model_b.predict(X))
🤝 Contributing
We welcome contributions in tutorials, metrics, automation, and visualizations. See docs/roadmap.md and docs/style_guide.md to get started.

🧠 Vision
Digamma Prime aims to become a universal symbolic standard for model auditing, drift detection, and interpretability.

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