PyStan

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 requires Python ≥3.7 running on Linux or 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.

import stan

schools_code = """
data {
  int<lower=0> J;         // number of schools
  real y[J];              // estimated treatment effects
  real<lower=0> sigma[J]; // standard error of effect estimates
}
parameters {
  real mu;                // population treatment effect
  real<lower=0> 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.

Metadata

Release files for pystan 3.4.0

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for pystan 3.4.0
File	Size	Uploaded
pystan-3.4.0.tar.gz	14.2 kB	Feb 15, 2022	Details

Built distribution (wheel)

Table of built distributions (wheels) for pystan 3.4.0
File	Interpreter	ABI	Platform	Reset
pystan-3.4.0-py3-none-any.whl 13.7 kB Feb 15, 2022	Python 3	none	any	Details

Total release size: 27.9 kB

Release files / pystan-3.4.0.tar.gz

Download URL	pystan-3.4.0.tar.gz
Size	14.2 kB
Tags	Source
SHA-256 checksum How to use checksums	`325e2fb0ab804555c05a603e0c9152ab11fcc3af01f3e9a9ff9fe9954b93184f`
BLAKE2b-256 checksum How to use checksums	`f8f8b751a4ae9478280a1884f161cf5f2bf8f48892e2eb7f34d73076e734cf8f`
Upload date	Feb 15, 2022
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`poetry/1.1.13 CPython/3.10.2 Linux/5.11.0-1028-azure`

Release files / pystan-3.4.0-py3-none-any.whl

Download URL	pystan-3.4.0-py3-none-any.whl
Size	13.7 kB
Tags	Python 3
SHA-256 checksum How to use checksums	`9d9f34a20c16f624a9002baaec174b4759230257f46691b9180cb13a67398691`
BLAKE2b-256 checksum How to use checksums	`aca4654b553891b29a58edd3d2ea728b19a986ff4e7198cde257e96e15eee7e4`
Upload date	Feb 15, 2022
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`poetry/1.1.13 CPython/3.10.2 Linux/5.11.0-1028-azure`