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CmdStanPy is a lightweight interface to Stan for Python users which provides the necessary objects and functions to compile a Stan program and fit the model to data using CmdStan.

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CmdStanPy

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CmdStanPy is a lightweight interface to Stan for Python users which provides the necessary objects and functions to compile a Stan program and run Stan's samplers.

Goals

  • Clean interface to Stan services so that CmdStanPy can keep up with Stan releases.

  • Provides complete control - all sampler arguments have corresponding named argument for CmdStanPy sampler function.

  • Easy to install,

    • minimal Python library dependencies: numpy, pandas
    • Python code doesn't interface directly with c++, only calls compiled executables
  • Modular - CmdStanPy produces a sample from the posterior, downstream modules do the analysis.

Docs

See https://cmdstanpy.readthedocs.io/en/latest/index.html

Source Repository

CmdStan's source-code repository is hosted here on GitHub.

Licensing

The CmdStanPy, CmdStan, and the core Stan C++ code are licensed under new BSD.

Example

::

import os
from cmdstanpy import CmdStanModel, cmdstan_path

# specify Stan file, create, compile CmdStanModel object
bernoulli_path = os.path.join(cmdstan_path(), 'examples', 'bernoulli', 'bernoulli.stan')
bernoulli_model = CmdStanModel(stan_file=bernoulli_path)


# specify data, fit the model
bernoulli_data = { "N" : 10, "y" : [0,1,0,0,0,0,0,0,0,1] }
bernoulli_fit = bernoulli_model.sample(chains=5, cores=3, data=bernoulli_data)

# summarize the results (wraps CmdStan `bin/stansummary`):
bernoulli_fit.summary()

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CmdStanPy is a lightweight interface to Stan for Python users which provides the necessary objects and functions to compile a Stan program and fit the model to data using CmdStan.

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  • Python 89.5%
  • Jupyter Notebook 5.2%
  • R 3.5%
  • Stan 1.6%
  • Dockerfile 0.2%