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25 search results found

Dowhy ⭐ 6,730

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.

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

Auton Survival ⭐ 271

Auton Survival - an open source package for Regression, Counterfactual Estimation, Evaluation and Phenotyping with Censored Time-to-Events

Scikit-learn compatible estimation of general graphical models

Input Output Hidden Markov Model (IOHMM) in Python

Toolbox ⭐ 104

A Java Toolbox for Scalable Probabilistic Machine Learning

pathpy is an OpenSource python package for the modeling and analysis of pathways and temporal networks using higher-order and multi-order graphical models

LoMRF is an open-source implementation of Markov Logic Networks

Benchpress ⭐ 52

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

Sparsebn ⭐ 37

Software for learning sparse Bayesian networks

Crfsuite ⭐ 36

Tree-Structured, First- and Higher-Order Linear Chain, and Semi-Markov CRFs

Graph Representation Analysis for Connected Embeddings

Easy Factor Graph ⭐ 24

General purpose C++ library for managing discrete factor graphs

REGAIN (Regularised Graphical Inference)

Markov random fields with covariates

Statnlp Framework ⭐ 17

C++ based implementation of StatNLP framework

Conditional Random Fields

Pythonbrmltoolbox ⭐ 10

Python 3.7 version of David Barber's MATLAB BRMLtoolbox

Rags2ridges ⭐ 7

Get ridge or die trying - 2 cents

An R Package for Estimating Time-Varying Graphical Models

Awesome Machine Learning ⭐ 5

Learning tutorial for machine learning beginners

Evaluating dependencies among random variables.

Implementation of Layered Graphical Model with demo code

Deepnotebooks ⭐ 5

DeepNotebooks is an automated statistical analysis tool build on top of SPNs. They are currently being developed by Claas Völcker at the ML group at TU Darmstadt.

Graphical_model_learning ⭐ 5

Learning graphical models, with a focus on causal models and learning from interventional data.

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