An Open Source Project from the Data to AI Lab, at MIT

Overview

• Website: https://sdv.dev

• Documentation: https://sdv.dev/Copulas

• Repository: https://github.com/sdv-dev/Copulas

• License: MIT

• Development Status: Pre-Alpha

Copulas is a Python library for modeling multivariate distributions and sampling from them using copula functions. Given a table containing numerical data, we can use Copulas to learn the distribution and later on generate new synthetic rows following the same statistical properties.

Some of the features provided by this library include:

• A variety of distributions for modeling univariate data.

• Multiple Archimedean copulas for modeling bivariate data.

• Gaussian and Vine copulas for modeling multivariate data.

• Automatic selection of univariate distributions and bivariate copulas.

Supported Distributions

Univariate

• Beta

• Gamma

• Gaussian

• Gaussian KDE

• Log-Laplace

• Student T

• Truncated Gaussian

• Uniform

Archimedean Copulas (Bivariate)

• Clayton

• Frank

• Gumbel

Multivariate

• Gaussian Copula

• D-Vine

• C-Vine

• R-Vine

Install

User Guides

• Introduction to Copulas
  • Probability Review
  • Copulas
• Univariate Distributions
  • Univariate Usage Example
  • Selecting the best Univariate
• Multivariate Distributions
  • Gaussian Multivariate
  • Vine Copulas

Advanced Usage

• Synthetic Data for Machine Learning
  • Loading the dataset
  • Generating synthetic data
  • Training a linear model

Indices and tables

• Index

• Module Index

• Search Page