{"categories":[{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/3"},{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/4"},{"label":"Bio-Informatics","url":"https://skillfed.io/packages/category/scientific-engineering-bio-informatics"}],"enrichment":{"capability":"pgmpy provides data structures and algorithms for causal discovery, causal inference, probabilistic inference, parameter learning, and model validation across Bayesian networks, DAGs, and structural equation models.","skillfed_tags":["causal-inference","graphical-models","probabilistic-reasoning"],"use_cases":["Learn causal structure from observational data using algorithms like PC, then estimate parameters and make predictions.","Compute posterior distributions conditioned on observed evidence in discrete or continuous Bayesian networks.","Generate synthetic data under specified interventions or counterfactual scenarios using do-calculus.","Validate whether a proposed causal structure is compatible with observed data using metrics.","Build hybrid models mixing discrete and continuous variables with functional relationships."],"what_it_does":"pgmpy is a Python toolkit for building and reasoning with causal and probabilistic graphical models. It implements data structures for DAGs, Bayesian networks, dynamic Bayesian networks, and structural equation models, along with algorithms for causal discovery, causal identification, probabilistic and causal inference, parameter estimation, model validation, and simulation.\n\nThe package provides a unified, composable API across algorithms and is scikit-learn compatible where applicable, allowing use in sklearn pipelines or as standalone tools. It handles discrete data, linear Gaussian data, and mixture models with arbitrary relationships. The library is actively maintained, supports Python 3.10 through 3.14, and depends on standard scientific Python libraries.","worth_installing":"Yes. pgmpy is actively maintained, has low install friction, carries no security vulnerabilities, and offers a comprehensive toolkit for causal and probabilistic reasoning with a scikit-learn-compatible API. It is suitable for research, education, and production use in causal inference and graphical modeling tasks. The MIT license imposes no restrictions."},"id":"pgmpy","links":{"html":"https://skillfed.io/packages/pgmpy","md":"https://skillfed.io/packages/pgmpy.md","pypi":"https://pypi.org/project/pgmpy/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-04-30","license_spdx":null,"license_treatment":"unclear","name":"pgmpy","python_support":"supports_current","summary":"Python Toolkit for Causal and Probabilistic Reasoning"},"popularity":{"monthly_downloads":957860,"position":4642,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"1.1.2"}