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    "info": {
        "author": "Elise Jennings",
        "author_email": "elise.jennings@gmail.com",
        "bugtrack_url": null,
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            "Environment :: Console",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: MIT License",
            "Operating System :: OS Independent",
            "Programming Language :: Python :: 2.7",
            "Topic :: Scientific/Engineering"
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        "description": "Approximate Bayesian computation (ABC) and so\ncalled \"likelihood free\" Markov chain Monte Carlo\ntechniques are popular methods for tackling parameter\ninference in scenarios where the likelihood is intractable or unknown.\nThese methods are called likelihood free as they are free from\nthe usual assumptions about the form of the likelihood e.g. Gaussian,\nas ABC aims to simulate samples from the parameter posterior distribution directly.\n``superABC`` is a python package that implements  the astroABC sampler using either the SNANA or the sncosmo Supernovae simulation\npackages and simulates samples from the posterior distribution using two distinct distance metrics.\n``superABC`` requires ``NumPy``,``SciPy`` and ``astroabc``. The SNANA and rootpy packages are required if using this as a forward model simulation.\nsncosmo, astropy and pandas are required if using sncosmo as the forward model simulation.",
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        "license": "MIT",
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        "name": "superabc",
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        "summary": "A Python sampling method for obtaining cosmological constraints from SN light curves using Approximate Bayesian Computation.",
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