Metadata-Version: 2.1
Name: cmdstanpy
Version: 0.9.6
Summary: Python interface to CmdStan
Home-page: https://github.com/stan-dev/cmdstanpy
Author: Stan Dev Team
License: UNKNOWN
Description: # CmdStanPy
        
        [![codecov](https://codecov.io/gh/stan-dev/cmdstanpy/branch/master/graph/badge.svg)](https://codecov.io/gh/stan-dev/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.
        
        - 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()
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: BSD License
Classifier: Operating System :: OS Independent
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Science/Research
Classifier: Natural Language :: English
Classifier: Programming Language :: Python
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Description-Content-Type: text/markdown
Provides-Extra: docs
Provides-Extra: all
Provides-Extra: tests
