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CausalityTools.jl

CausalityTools is a Julia package providing algorithms for detecting causal relations in complex systems based on time series data.

Goals

  • Provide a comprehensive, easy-to-use framework for the detection of directional causal influences in complex dynamical systems from time series.
  • Functional and efficient implementations of causality detection algorithm, with thorough documentation and references to primary literature.
  • Extensive library of example dynamical systems for testing algorithm performance.
  • Worked examples for all algorithms.

Package status

The package and documentation is under active development. Breaking changes may occur in CausalityTools and its dependencies until the 1.0 release.

Package structure

CausalityTools.jl brings together the following packages into one environment:

package functionality version build
StateSpaceReconstruction.jl Fully flexible state space reconstructions (embeddings), partitioning routines (variable-width rectangular, and triangulations), and partition refinement (equal-volume splitting of simplices). 0.3.1 Build Status
TimeseriesSurrogates.jl Generate surrogate data from time series. 0.2.1 Build Status
TransferEntropy.jl Transfer entropy estimators. 0.3.1 Build Status
PerronFrobenius.jl Transfer (Perron-Frobenius) operator estimators. 0.2.2 Build Status
Simplices.jl Exact simplex intersections in N dimensions. 0.2.2 Build Status
CrossMappings.jl Exact simplex intersections in N dimensions. 0.2.0 Build Status

Contributors

  • DynamicalSystems.jl provides a range of tools for exploring nonlinear dynamics and chaos, both for synthetic and observed systems. We provide seamless interaction with Dataset outputs from DynamicalSystems. Most of our example systems are also implemented as DiscreteDynamicalSystems or ContinuousDynamicalSystems from DynamicalSystems.