{"categories":[{"label":"Information Analysis","url":"https://skillfed.io/packages/category/scientific-engineering-information-analysis"}],"enrichment":{"capability":"CmdStanPy provides a pure-Python interface to the Stan probabilistic programming language, enabling you to compile Stan models and run Bayesian inference algorithms without direct C++ interaction.","skillfed_tags":["bayesian-inference","probabilistic-programming","mcmc"],"use_cases":["Fit Bayesian hierarchical models to experimental or observational data and extract posterior samples for inference.","Develop and test Stan models iteratively during research, with automatic compilation and temporary output storage.","Run multiple inference jobs across machines or clusters by scripting CmdStanPy to distribute analysis reproducibly.","Compare inference algorithms (MCMC, variational, optimization) on the same model without rewriting code.","Integrate Bayesian inference into Python data pipelines alongside pandas and numpy workflows."],"what_it_does":"CmdStanPy is a lightweight wrapper around CmdStan, the command-line interface to the Stan probabilistic programming language. It lets you write statistical models in Stan's modeling language, compile them to executables, and run Bayesian inference\u2014MCMC sampling, variational inference, and optimization\u2014all from Python. The package handles model compilation, data passing, and result parsing, while keeping memory overhead low by default and storing output on a temporary filesystem during development.\n\nThe interface is designed for both iterative model development and production workflows. It depends on numpy and pandas for numerical operations and data handling, plus tqdm for progress tracking and stanio for I/O. Because it calls compiled Stan executables rather than embedding C++ directly, installation is straightforward and the package remains modular\u2014you use CmdStanPy to generate posterior samples, then use other tools for analysis and visualization.","worth_installing":"Yes. CmdStanPy is actively maintained, has no known vulnerabilities, minimal dependencies, and a permissive license. Install it if you need to run Stan models from Python for Bayesian inference, statistical modeling, or probabilistic programming. The low friction and modular design make it a solid choice for both research and production use."},"id":"cmdstanpy","links":{"html":"https://skillfed.io/packages/cmdstanpy","md":"https://skillfed.io/packages/cmdstanpy.md","pypi":"https://pypi.org/project/cmdstanpy/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2025-10-20","license_spdx":null,"license_treatment":"permissive","name":"cmdstanpy","python_support":"supports_current","summary":"Python interface to CmdStan"},"popularity":{"monthly_downloads":6992043,"position":1800,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"1.3.0"}