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

regain

REGAIN (Regularised Graphical Inference)

spflow

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

functional-connectivity

Sensing the functional connectivity of the brain

depynd

Evaluating dependencies among random variables.

robopy

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

pgmpy-rcr

Python Toolkit for Causal and Probabilistic Reasoning

iohmm

A python library for Input Output Hidden Markov Models

lgnpy

Linear Gaussian Networks - Inference, Parameter Learning and Representation

cstrees

CSlearn: a package for context-specific causal models

dgcnn

TensorFlow 2 implementation of Deep Graph Convolutional Neural Networks.

skggm

Scikit-learn compatible estimation of general graphical models

junctiontree

Junction tree and belief propagation algorithms

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.

skggm2

Gaussian graphical models for scikit-learn.

graphtime

Dynamic Graphical Model Estimation