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"

spflow

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

regain

REGAIN (Regularised Graphical Inference)

gglasso

Algorithms for Single and Multiple Graphical Lasso problems.

depynd

Evaluating dependencies among random variables.

robopy

A Python 3 implementation of Peter Corke's Robotics Toolbox

lgnpy

Linear Gaussian Networks - Inference, Parameter Learning and Representation

functional-connectivity

Sensing functional connectivity in the brain, in Python

iohmm

A python library for Input Output Hidden Markov Models

pgmpy-rcr

Python Toolkit for Causal and Probabilistic Reasoning

skggm

Scikit-learn compatible estimation of general graphical models

skggm2

Scikit-learn compatible estimation of general graphical models

cstrees

CSlearn: a package for context-specific causal models

dgcnn

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

junctiontree

The junction tree algorithm for (discrete) factor graphs

graphtime

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

dortmund2array

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