Welcome to CopulaFurtif’s documentation!

CopulaFurtif is a modular pipeline for modeling, fitting, and analyzing bivariate copulas.

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📚 Available Sections:

Getting Started

• Installation
  • ⚙️ Prerequisites
  • 📦 Installation with Poetry (recommended)
  • 📦 Installation with pip (alternative)
  • 💡 (Optional): install pre-commit
  • 🧪 Run the tests
  • 📚 Generate the documentation
  • 📂 Simplified folder structure
• Basic Usage
  • 🧱 Creating Copulas
  • 📊 Input Data
  • 📈 Accessing Basic Methods
  • 🔬 Diagnostics
• Copula Fitting
  • 🧪 Goal
  • ⚙️ Available Tools
  • 🚀 Example: CMLE
  • 📦 Example: MLE with marginals
  • 🔁 Example: IFM
  • 🔍 Optimization Options
  • 📌 Tips
• Visualization
  • 🌡️ Residual Heatmap (Empirical - Model)
  • 📈 Conditional Curves
  • 📊 Copula Benchmarking
  • 🎯 Integrated Visual Summary
• Extending the Pipeline: Adding a Copula
  • 🧱 Steps to Add a Copula
  • 🧪 Full Example
  • 📌 Best Practices

• core
  • core package

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🚀 Quickstart

Here’s a quick example:

from CopulaFurtif import CopulaFactory
from CopulaFurtif.core.copulas.application.use_cases.fit_copula import FitCopulaUseCase

copula = CopulaFactory.create("gaussian")
data = [[0.1, 0.2], [0.4, 0.5], [0.8, 0.9]]  # example

fit_result = FitCopulaUseCase().fit_cmle(data, copula)
print("Params:", copula.parameters)