Associations.jl

Algorithms for quantifying associations, independence testing and causal inference from data.
Author JuliaDynamics
Popularity
147 Stars
Updated Last
23 Days Ago
Started In
May 2018

Associations

CI codecov DOI

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

All further information is provided in the documentation, 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, 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.