https://github.com/JuliaDynamics/Associations.jl

Algorithms for quantifying associations, independence testing and causal inference from data.
https://github.com/JuliaDynamics/Associations.jl

associations causal-graphs causal-inference conditional-mutual-information cross-mapping distance-correlation entropy hacktoberfest independence-testing julia julia-language mutual-information network-inference partial-mutual-information predictive-asymmetry surrogate-data time-series transfer-entropy

Last synced: 3 months ago
JSON representation

Algorithms for quantifying associations, independence testing and causal inference from data.

• Host: GitHub
• URL: https://github.com/JuliaDynamics/Associations.jl
• Owner: JuliaDynamics
• License: other
• Created: 2018-05-30T12:58:18.000Z (over 6 years ago)
• Default Branch: main
• Last Pushed: 2024-08-02T01:18:50.000Z (3 months ago)
• Last Synced: 2024-08-02T01:22:44.636Z (3 months ago)
• Topics: associations, causal-graphs, causal-inference, conditional-mutual-information, cross-mapping, distance-correlation, entropy, hacktoberfest, independence-testing, julia, julia-language, mutual-information, network-inference, partial-mutual-information, predictive-asymmetry, surrogate-data, time-series, transfer-entropy
• Language: Julia
• Homepage: https://juliadynamics.github.io/Associations.jl/stable/
• Size: 40.5 MB
• Stars: 144
• Watchers: 5
• Forks: 12
• Open Issues: 50
• Metadata Files:
  • Readme: README.md
  • Changelog: changelog.md
  • License: LICENSE.md

Awesome Lists containing this project

• awesome-sciml - JuliaDynamics/CausalityTools.jl: Algorithms for causal inference and the detection of dynamical coupling from time series, and for approximation of the transfer operator and invariant measures.

README

        
# Associations

[![CI](https://github.com/juliadynamics/Associations.jl/workflows/CI/badge.svg)](https://github.com/JuliaDynamics/Associations.jl/actions)

[![](https://img.shields.io/badge/docs-latest_tagged-blue.svg)](https://juliadynamics.github.io/Associations.jl/stable/)

[![](https://img.shields.io/badge/docs-dev_(main)-blue.svg)](https://juliadynamics.github.io/Associations.jl/dev/)

[![codecov](https://codecov.io/gh/JuliaDynamics/Associations.jl/branch/main/graph/badge.svg?token=0b71n6x6AP)](https://codecov.io/gh/JuliaDynamics/Associations.jl)

[![DOI](https://zenodo.org/badge/135443027.svg)](https://zenodo.org/badge/latestdoi/135443027)

Associations.jl is a package for quantifying associations, independence testing and causal inference.

All further information is provided in the

[documentation](https://juliadynamics.github.io/Associations.jl/dev), which you can either

find online or build locally by running the `docs/make.jl` file.

## Key features

- **Association API**: includes measures and their estimators for pairwise, conditional and other forms of 

    association from conventional statistics, from dynamical systems theory, and from information theory: partial correlation, distance correlation, (conditional) mutual information, transfer entropy, convergent cross mapping and a lot more!

- **Independence testing API**, which is automatically compatible with

    every association measure estimator implemented in the package. 

- **Causal (network) inference API** integrating the association measures and independence testing framework.

## Addititional features

Extending on features from [ComplexityMeasures.jl](https://github.com/JuliaDynamics/ComplexityMeasures.jl),

we also offer 

- Discretization API for multiple (multivariate) input datasets.

- Multivariate counting and probability estimation API.

- Multivariate information measure API

## Installation

To install the package, run `import Pkg; Pkg.add("Associations")`.

*Previously, this package was called CausalityTools.jl*.