A Snakemake workflow to run and benchmark structure learning (a.k.a. causal discovery) algorithms for probabilistic graphical models.

Python 3.7 version of David Barber's MATLAB BRMLtoolbox

Bayesian structure learning and classification in decomposable graphical models.

Graph: Representation, Learning, and Inference Methods

BN++ Data Structures and Algorithms in C++ for Bayesian Networks

This R-package is for learning the structure of the type of graphical models called t-cherry trees from data. The structure is determined either directly from data or by increasing the order of a lower order t-cherry tree.

Repository for tasks like Representation, Inference and Learning of Probabilistic Graphical Models.

This is a collection of algorithms and models written in Python for probabilistic programming. The main focus of the package is on Bayesian reasoning by using Bayesian networks, Markov networks, and their mixing.