https://github.com/stan-dev/pystan

PyStan, a Python interface to Stan, a platform for statistical modeling. Documentation: https://pystan.readthedocs.io
https://github.com/stan-dev/pystan

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PyStan, a Python interface to Stan, a platform for statistical modeling. Documentation: https://pystan.readthedocs.io

• Host: GitHub
• URL: https://github.com/stan-dev/pystan
• Owner: stan-dev
• License: isc
• Created: 2017-09-17T14:13:04.000Z (about 7 years ago)
• Default Branch: main
• Last Pushed: 2024-04-12T14:20:16.000Z (7 months ago)
• Last Synced: 2024-04-22T02:53:53.779Z (7 months ago)
• Language: Python
• Homepage:
• Size: 210 KB
• Stars: 313
• Watchers: 13
• Forks: 57
• Open Issues: 13
• Metadata Files:
  • Readme: README.rst
  • Funding: .github/FUNDING.yml
  • License: LICENSE

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README

        
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PyStan

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**PyStan** is a Python interface to Stan, a package for Bayesian inference.

Stan® is a state-of-the-art platform for statistical modeling and

high-performance statistical computation. Thousands of users rely on Stan for

statistical modeling, data analysis, and prediction in the social, biological,

and physical sciences, engineering, and business.

Notable features of PyStan include:

* Automatic caching of compiled Stan models

* Automatic caching of samples from Stan models

* An interface similar to that of RStan

* Open source software: ISC License

Getting started

===============

Install PyStan with ``pip install pystan``. PyStan runs on Linux and macOS. You will also need a C++ compiler such as gcc ≥9.0 or clang ≥10.0.

The following block of code shows how to use PyStan with a model which studied coaching effects across eight schools (see Section 5.5 of Gelman et al (2003)). This hierarchical model is often called the "eight schools" model.

.. code-block:: python

    import stan

    schools_code = """

    data {

      int J;         // number of schools

      array[J] real y;              // estimated treatment effects

      array[J] real sigma; // standard error of effect estimates

    }

    parameters {

      real mu;                // population treatment effect

      real tau;      // standard deviation in treatment effects

      vector[J] eta;          // unscaled deviation from mu by school

    }

    transformed parameters {

      vector[J] theta = mu + tau * eta;        // school treatment effects

    }

    model {

      target += normal_lpdf(eta | 0, 1);       // prior log-density

      target += normal_lpdf(y | theta, sigma); // log-likelihood

    }

    """

    schools_data = {"J": 8,

                    "y": [28,  8, -3,  7, -1,  1, 18, 12],

                    "sigma": [15, 10, 16, 11,  9, 11, 10, 18]}

    posterior = stan.build(schools_code, data=schools_data)

    fit = posterior.sample(num_chains=4, num_samples=1000)

    eta = fit["eta"]  # array with shape (8, 4000)

    df = fit.to_frame()  # pandas `DataFrame`

Citation

========

We appreciate citations as they let us discover what people have been doing

with the software. Citations also provide evidence of use which can help in

obtaining grant funding.

To cite PyStan in publications use:

Riddell, A., Hartikainen, A., & Carter, M. (2021). PyStan (3.0.0). https://pypi.org/project/pystan

Or use the following BibTeX entry::

    @misc{pystan,

      title = {pystan (3.0.0)},

      author = {Riddell, Allen and Hartikainen, Ari and Carter, Matthew},

      year = {2021},

      month = mar,

      howpublished = {PyPI}

    }

Please also cite Stan.