CmdStanPy
CmdStanPy is a lightweight interface to Stan for Python users which provides the necessary objects and functions to do Bayesian inference given a probability model written as a Stan program and data. Under the hood, CmdStanPy uses the CmdStan command line interface to compile and run a Stan program.
Goals
-
Clean interface to Stan services so that CmdStanPy can keep up with Stan releases.
-
Provide access to all CmdStan inference methods.
-
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 MCMC sample (or point estimate) from the posterior; other packages do analysis and visualization.
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 cmdstan_path, CmdStanModel
# specify locations of Stan program file and data
bernoulli_stan = os.path.join(cmdstan_path(), 'examples', 'bernoulli', 'bernoulli.stan')
bernoulli_data = os.path.join(cmdstan_path(), 'examples', 'bernoulli', 'bernoulli.data.json')
# instantiate a model; compiles the Stan program by default
bernoulli_model = CmdStanModel(stan_file=bernoulli_stan)
# obtain a posterior sample from the model conditioned on the data
bernoulli_fit = bernoulli_model.sample(chains=4, data=bernoulli_data)
# summarize the results (wraps CmdStan `bin/stansummary`):
bernoulli_fit.summary()
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