Python bindings for Frog expose Dutch NLP functions such as tagging, lemmatization, named entity recognition, and parsing.
Open source
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pgmpy provides Python tools for causal inference and probabilistic reasoning.
About pgmpy
pgmpy is a Python toolkit for causal and probabilistic reasoning using graphical models. It provides data structures for DAGs, PDAGs, MAGs, PAGs, Bayesian Networks, Dynamic Bayesian Networks, and Structural Equation Models, with algorithms for causal discovery, identification, inference, validation, parameter estimation, and simulation.
Algorithms use a unified composable API and are scikit-learn compatible when possible, for direct use, use in sklearn pipelines, or as a base for higher-level tools. It is open source and installable from PyPI and conda-forge.
Key features
- Learn model structure from data with optional expert knowledge
- Estimate model parameters such as conditional probability distributions
- Compute posterior distributions conditioned on observed evidence
- Compute interventional distributions for causal inference
- Generate synthetic data under specified evidence or interventions
- Provide example datasets and models from various sources
Best for: Python users working with causal discovery, inference, and probabilistic graphical models.
Good for
- Open source with modular and extensible unified API
- Scikit-learn compatible when possible and usable in sklearn pipelines
- Installable from PyPI and conda-forge with example and tutorial notebooks
pgmpy at a glance
- Website
- github.com/pgmpy/pgmpy
- Category
- Development › Programming Languages
- Type
- Open-source project
- Pricing
- Not confirmed. Check the site
- Open source
- Yes · Source code
- Language
- English
- Last checked
- Oct 5, 2026 · Online when checked
- Listed
- Oct 5, 2026
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pgmpy FAQ
How is pgmpy installed?
From PyPI with pip install pgmpy, or from conda-forge with conda install conda-forge::pgmpy.
What types of graphical models does it support?
DAGs, PDAGs, MAGs, PAGs, Bayesian Networks, Dynamic Bayesian Networks, and Structural Equation Models.
Does it work with scikit-learn?
Algorithms are scikit-learn compatible when possible and can be combined in sklearn pipelines.
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