JIDT: Java Information Dynamics Toolkit

JIDT is a flagship toolkit for information theory in complex systems, originally designed by [Joe Lizier]( It's writen in super-duper-portable Java, which means you can simply download it and run it from Python, Matlab, or R -- without installation. Damn that's convenient.

EntRate: Entropy rate estimators for neuroscience

This library implements various estimators of entropy rate on continuous and discrete data. Work in progress, suggestions welcome!

FastDMF: Fast Dynamic Mean Field model simulator

This library implements an extended version of Deco's DMF simulator of brain dynamics using a multi-threaded C++ backend (via [Eigen](, and provides both Python and Matlab interfaces. This code is around 5-10x faster and consumes around 1000x less memory than previous implementations.

SynDisc: Synergistic information via data disclosure

This Python package implements the synergy measure (and corresponding information decomposition) in our 2020 *J Physics A* paper. In essence, synergy is defined as the maximum information transmittable through a 'synergistic channel', and the optimisation is done with linear programming on the vertices of a probability polytope.

IDTxl: Information Dynamics Toolkit xl

IDTxl is a dedicated Python package for the estimation of directed information transfer networks from data. It is extremely data-efficient: it can accurately estimate networks between hundreds of nodes with as few as 1000 time points.