JIDT is a flagship toolkit for information theory in complex systems, originally designed by [Joe Lizier](https://lizier.me/joseph). 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.
This library implements various estimators of entropy rate on continuous and discrete data. Work in progress, suggestions welcome!
This library implements an extended version of Deco's DMF simulator of brain dynamics using a multi-threaded C++ backend (via [Eigen](https://eigen.tuxfamily.org/index.php)), and provides both Python and Matlab interfaces. This code is around 5-10x faster and consumes around 1000x less memory than previous implementations.
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 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.