IDTxl: The Information Dynamics Toolkit xl: a Python package for the efficient analysis of multivariate information dynamics in networks

Abstract

The Information Dynamics Toolkit xl (IDTxl) is a comprehensive software package for efficient inference of networks and their node dynamics from multivariate time series data using information theory. IDTxl provides functionality to estimate the following measures: 1) For network inference: multivariate transfer entropy (TE)/Granger causality (GC), multivariate mutual information (MI), bivariate TE/GC, bivariate MI 2) For analysis of node dynamics: active information storage (AIS), partial information decomposition (PID) IDTxl implements estimators for discrete and continuous data with parallel computing engines for both GPU and CPU platforms. Written for Python3.4.3+.

arXiv comment4 pages

References in corpus (6)

• Measuring Information Transfer
• Estimating Mutual Information
• JIDT: An information-theoretic toolkit for studying the dynamics of complex systems
• Quantifying unique information
• A framework for the local information dynamics of distributed computation in complex systems
• BROJA-2PID: A robust estimator for bivariate partial information decomposition

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• Generalised Measures of Multivariate Information Content
• Deriving pairwise transfer entropy from network structure and motifs
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