{"categories":[{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/5"},{"label":"Mathematics","url":"https://skillfed.io/packages/category/scientific-engineering-mathematics/2"}],"enrichment":{"capability":"Extends PyMC with specialized distributions, alternative inference methods, and statespace models that are too specialized for the core library but useful enough to avoid reimplementing.","skillfed_tags":["bayesian-inference","time-series-modeling","probabilistic-programming"],"use_cases":["Fit time-series models like SARIMAX or structural time series with Kalman filtering when standard PyMC distributions are insufficient.","Use alternative inference methods such as Pathfinder or INLA for faster or more accurate posterior approximation in specialized models.","Apply automatic marginalization to handle finite discrete or conjugate variables exactly or via Laplace approximation.","Model rare or extreme-value phenomena using specialized distributions like GenExtreme or GeneralizedPoisson.","Implement Markov chain distributions for state-space or sequential modeling tasks.","Experiment with advanced Bayesian techniques before they mature into the core PyMC library."],"what_it_does":"PyMC Extras is a companion library to PyMC that houses specialized statistical methods and distributions too niche for the core library but mature enough to be useful. It provides alternative inference algorithms (Pathfinder, DADVI, INLA, Laplace approximation, better MAP estimation), additional probability distributions (DiscreteMarkovChain, GeneralizedPoisson, GenExtreme), and statespace models with Kalman filtering (SARIMAX, VARMAX, ETS, structural time series). The library mirrors PyMC's namespaces so distributions and methods integrate seamlessly into existing PyMC models.\n\nIt is maintained by the PyMC team and designed as an incubator: functionality that proves widely useful may graduate to the main PyMC repository. The package depends on pymc, pytensor, arviz, pydantic, preliz, and better-optimize, and targets modern Python versions (3.12, 3.13, 3.14). It carries no known vulnerabilities and is actively developed.","worth_installing":"Yes. PyMC Extras is actively maintained by the PyMC team, carries no known vulnerabilities, installs with low friction, and is licensed permissively. Install it if you need specialized distributions, alternative inference methods, or statespace models beyond what PyMC core offers. It is production-ready (Development Status 5) and well-integrated with the PyMC ecosystem."},"id":"pymc-extras","links":{"html":"https://skillfed.io/packages/pymc-extras","md":"https://skillfed.io/packages/pymc-extras.md","pypi":"https://pypi.org/project/pymc-extras/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-07-28","license_spdx":null,"license_treatment":"permissive","name":"pymc-extras","python_support":"supports_current","summary":"A home for new additions to PyMC, which may include unusual probability distribitions, advanced model fitting algorithms, or any code that may be inappropriate to include in the pymc repository, but may want to be made available to users."},"popularity":{"monthly_downloads":257653,"position":8438,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.14.0"}