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setup.py
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#!/usr/bin/env python
from setuptools import setup
DISTNAME = 'pymc'
DESCRIPTION = "PyMC 3"
LONG_DESCRIPTION = """Bayesian estimation, particularly using Markov chain Monte Carlo (MCMC), is an increasingly relevant approach to statistical estimation. However, few statistical software packages implement MCMC samplers, and they are non-trivial to code by hand. ``pymc`` is a python package that implements the Metropolis-Hastings algorithm as a python class, and is extremely flexible and applicable to a large suite of problems. ``pymc`` includes methods for summarizing output, plotting, goodness-of-fit and convergence diagnostics."""
MAINTAINER = 'John Salvatier'
MAINTAINER_EMAIL = '[email protected]'
AUTHOR = 'John Salvatier and Christopher Fonnesbeck'
AUTHOR_EMAIL = '[email protected]'
URL = "http://github.com/pymc-devs/pymc"
LICENSE = "Apache License, Version 2.0"
VERSION = "3.0"
classifiers = ['Development Status :: 3 - Alpha',
'Programming Language :: Python',
'Programming Language :: Python :: 2',
'Programming Language :: Python :: 3',
'Programming Language :: Python :: 2.7',
'Programming Language :: Python :: 3.3',
'License :: OSI Approved :: Apache Software License',
'Intended Audience :: Science/Research',
'Topic :: Scientific/Engineering',
'Topic :: Scientific/Engineering :: Mathematics',
'Operating System :: OS Independent']
required = ['numpy>=1.7.1', 'scipy>=0.12.0', 'matplotlib>=1.2.1',
'Theano==0.6.0']
if __name__ == "__main__":
setup(name=DISTNAME,
version=VERSION,
maintainer=MAINTAINER,
maintainer_email=MAINTAINER_EMAIL,
description=DESCRIPTION,
license=LICENSE,
url=URL,
long_description=LONG_DESCRIPTION,
packages=['pymc', 'pymc.distributions',
'pymc.step_methods', 'pymc.tuning',
'pymc.tests', 'pymc.glm'],
classifiers=classifiers,
install_requires=required)