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

Patricia Wollstadt, Joseph T. Lizier, Raul Vicente, Conor Finn, Mario Martínez-Zarzuela +3 more
7/27/2018
cs.IT

Official code: pwollstadt/IDTxl (297 stars, GPL-3.0, last commit 2026-04-23).

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+.

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Code Implementations(1)

pwollstadt/IDTxlOfficial100%
GPL-3.0

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.

29784C, TeXJul 16, 20156 months agoGPL-3.0
active-information-storagecomplex-systemsinformation-theorypartial-information-decompositiontransfer-entropy

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Cite this paper

@article{wollstadt2018idtxl,
  title  = {IDTxl: The Information Dynamics Toolkit xl: a Python package for the efficient analysis of multivariate information dynamics in networks},
  author = {Patricia Wollstadt and Joseph T. Lizier and Raul Vicente and Conor Finn and Mario Martínez-Zarzuela and Pedro Mediano and Leonardo Novelli and Michael Wibral},
  year   = {2018},
  eprint = {1807.10459},
  archivePrefix = {arXiv},
  url    = {https://arxiv.org/abs/1807.10459}
}

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