1Metadata-Version: 1.1
2Name: pystan
3Version: 2.19.0.0
4Summary: Python interface to Stan, a package for Bayesian inference
5Home-page: https://github.com/stan-dev/pystan
6Author: PyStan Developers
7Author-email: stan-users@googlegroups.com
8License: GPLv3
9Description: PyStan: The Python Interface to Stan
10        ====================================
11
12        .. image:: https://raw.githubusercontent.com/stan-dev/logos/master/logo.png
13            :alt: Stan logo
14            :scale: 50 %
15
16        |pypi| |travis| |appveyor| |zenodo|
17
18        **PyStan** provides a Python interface to Stan, a package for Bayesian inference
19        using the No-U-Turn sampler, a variant of Hamiltonian Monte Carlo.
20
21        For more information on `Stan <http://mc-stan.org>`_ and its modeling language,
22        see the Stan User's Guide and Reference Manual at `http://mc-stan.org/
23        <http://mc-stan.org/>`_.
24
25        Important links
26        ---------------
27
28        - HTML documentation: https://pystan.readthedocs.org
29        - Issue tracker: https://github.com/stan-dev/pystan/issues
30        - Source code repository: https://github.com/stan-dev/pystan
31        - Stan: http://mc-stan.org/
32        - Stan User's Guide and Reference Manual (pdf) available at http://mc-stan.org
33
34        Related projects
35        ----------------
36
37        - Scikit-learn integration: `pystan-sklearn <https://github.com/rgerkin/pystan-sklearn>`_ by @rgerkin.
38
39        Similar projects
40        ----------------
41
42        - PyMC: http://pymc-devs.github.io/pymc/
43
44        Detailed Installation Instructions
45        ----------------------------------
46        Detailed installation instructions can be found in the
47        `doc/installation_beginner.md <https://github.com/chendaniely/pystan/blob/develop/doc/installation_beginner.rst/>`_ file.
48
49        Quick Installation (Linux and macOS)
50        ------------------------------------
51
52        `NumPy  <http://www.numpy.org/>`_ and `Cython <http://www.cython.org/>`_
53        (version 0.22 or greater) are required. `matplotlib <http://matplotlib.org/>`_
54        is optional.
55
56        PyStan and the required packages may be installed from the `Python Package Index
57        <https://pypi.python.org/pypi>`_ using ``pip``.
58
59        ::
60
61           pip install pystan
62
63        Alternatively, if Cython (version 0.22 or greater) and NumPy are already
64        available, PyStan may be installed from source with the following commands
65
66        ::
67
68           git clone --recursive https://github.com/stan-dev/pystan.git
69           cd pystan
70           python setup.py install
71
72        If you encounter an ``ImportError`` after compiling from source, try changing
73        out of the source directory before attempting ``import pystan``. On Linux and
74        OS X ``cd /tmp`` will work.
75
76        Example
77        -------
78
79        ::
80
81            import pystan
82            import numpy as np
83            import matplotlib.pyplot as plt
84
85            schools_code = """
86            data {
87                int<lower=0> J; // number of schools
88                real y[J]; // estimated treatment effects
89                real<lower=0> sigma[J]; // s.e. of effect estimates
90            }
91            parameters {
92                real mu;
93                real<lower=0> tau;
94                real eta[J];
95            }
96            transformed parameters {
97                real theta[J];
98                for (j in 1:J)
99                    theta[j] = mu + tau * eta[j];
100            }
101            model {
102                eta ~ normal(0, 1);
103                y ~ normal(theta, sigma);
104            }
105            """
106
107            schools_dat = {'J': 8,
108                           'y': [28,  8, -3,  7, -1,  1, 18, 12],
109                           'sigma': [15, 10, 16, 11,  9, 11, 10, 18]}
110
111            sm = pystan.StanModel(model_code=schools_code)
112            fit = sm.sampling(data=schools_dat, iter=1000, chains=4)
113
114            print(fit)
115
116            eta = fit.extract(permuted=True)['eta']
117            np.mean(eta, axis=0)
118
119            # if matplotlib is installed (optional, not required), a visual summary and
120            # traceplot are available
121            fit.plot()
122            plt.show()
123
124        .. |pypi| image:: https://badge.fury.io/py/pystan.png
125            :target: https://badge.fury.io/py/pystan
126            :alt: pypi version
127
128        .. |travis| image:: https://travis-ci.org/stan-dev/pystan.png?branch=master
129            :target: https://travis-ci.org/stan-dev/pystan
130            :alt: travis-ci build status
131
132        .. |appveyor| image:: https://ci.appveyor.com/api/projects/status/github/stan-dev/pystan?branch=master
133            :target: https://ci.appveyor.com/project/ariddell/pystan/branch/master
134            :alt: appveyor-ci build status
135        .. |zenodo| image:: https://zenodo.org/badge/10256919.svg
136            :target: https://zenodo.org/badge/latestdoi/10256919
137            :alt: zenodo citation DOI
138
139Platform: any
140Classifier: Programming Language :: Python
141Classifier: Programming Language :: Python :: 2
142Classifier: Programming Language :: Python :: 3
143Classifier: Programming Language :: Cython
144Classifier: Development Status :: 4 - Beta
145Classifier: Environment :: Console
146Classifier: Operating System :: OS Independent
147Classifier: Intended Audience :: Developers
148Classifier: Intended Audience :: Science/Research
149Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
150Classifier: Topic :: Scientific/Engineering
151Classifier: Topic :: Scientific/Engineering :: Information Analysis
152