Graphical Models Python Packages

pgmpy

Python Toolkit for Causal and Probabilistic Reasoning

dowhy

DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language for causal inference, combining causal graphical models and potential outcomes frameworks.

autofit

PyAutoFit: Classy Probabilistic Programming

dagma

A Python 3 package for learning Bayesian Networks (DAGs) from data. Official implementation of the paper "DAGMA: Learning DAGs via M-matrices and a Log-Determinant Acyclicity Characterization"

gglasso

A Python package for General Graphical Lasso computation

spflow

Sum Product Flow: An Easy and Extensible Library for Sum-Product Networks

regain

REGAIN (Regularised Graphical Inference)

depynd

Evaluating dependencies among random variables.

robopy

Robopy is a python port for Robotics Toolbox in Matlab created by Peter Corke

iohmm

Input Output Hidden Markov Model (IOHMM) in Python

lgnpy

Linear Gaussian Bayesian Networks - Inference, Parameter Learning and Representation. 🖧

pgmpy-rcr

Python Toolkit for Causal and Probabilistic Reasoning

skggm

Scikit-learn compatible estimation of general graphical models

functional-connectivity

Sensing the functional connectivity of the brain

skggm2

Scikit-learn compatible estimation of general graphical models

cstrees

CSlearn: a package for context-specific causal models

junctiontree

The junction tree algorithm for (discrete) factor graphs

dgcnn

Clean & Documented TF2 implementation of "An end-to-end deep learning architecture for graph classification" (M. Zhang et al., 2018).

dortmund2array

Tool to convert datasets from "Benchmark Data Sets for Graph Kernels" (K. Kersting et al., 2016) into a format suitable for deep learning research.

graphtime

A Package for Dynamic Graphical Model Estimation. Future versions in R coming soon