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

TL;DR

IDTxl is a Python toolkit that enables efficient inference and analysis of multivariate information dynamics in networks from time series data, supporting various information-theoretic measures with parallel computing capabilities.

Contribution

It introduces a comprehensive Python package that integrates multiple information-theoretic measures for network inference and node dynamics analysis, optimized for GPU and CPU parallel processing.

Findings

Supports estimation of transfer entropy, Granger causality, and mutual information.

Provides tools for analyzing node dynamics like active information storage and partial information decomposition.

Enables efficient processing of large multivariate time series data.

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+.