jaysulk / 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.

CausalityTools

CausalityTools.jl provides methods for causal inference and detection of dynamical coupling based on time series.

Check out the documentation for more information!

Key tools

• A easy-to-use framework for estimating information theoretic measures, such as transfer entropy, predictive asymmetry, generalized entropy and mutual information.
• Convergent cross mapping, pairwise asymmetric inference, S-measure and joint distance distribution.
• Surrogate data generation.

Installation

CausalityTools.jl is a registered julia package, you can therefore add the latest tagged release by running the following lines in the Julia console.

import Pkg; Pkg.add("CausalityTools")

For the latest development version of the package, add the package by referring directly to the GitHub repository.

import Pkg; Pkg.add(url="https://github.com/juliadynamics/CausalityTools.jl/", rev="master")