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Algorithms for detecting associations, dynamical influences and causal inference from data.

Home Page: https://juliadynamics.github.io/CausalityTools.jl/dev/

License: Other

Julia 87.12% Jupyter Notebook 12.88%

causalitytools.jl's Introduction

CausalityTools

CI codecov DOI

CausalityTools.jl is a package for quantifying associations and dynamical coupling between datasets, 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 measures from conventional statistics, information theory and dynamical systems theory, for example distance correlation, mutual information, transfer entropy, convergent cross mapping and a lot more!
  • A dedicated API for independence testing, which comes with automatic compatibility with every measure-estimator combination you can think of. For example, we offer the generic SurrogateTest, which is fully compatible with TimeseriesSurrogates.jl, and the LocalPermutationTest for conditional indepencence testing.
  • A dedicated API for causal network inference based on these measures and independence tests.

Installation

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

causalitytools.jl's People

Contributors

kahaaga avatar github-actions[bot] avatar romeov avatar datseris avatar juliatagbot avatar imgbot[bot] avatar tormolle avatar

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