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            "Programming Language :: Python :: 2",
            "Programming Language :: Python :: 2.6",
            "Programming Language :: Python :: 2.7",
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        "description": "=========\npydftools\n=========\n\n\n.. image:: https://img.shields.io/pypi/v/pydftools.svg\n        :target: https://pypi.python.org/pypi/pydftools\n\n.. image:: https://img.shields.io/travis/steven-murray/pydftools.svg\n        :target: https://travis-ci.org/steven-murray/pydftools\n\n.. image:: https://readthedocs.org/projects/pydftools/badge/?version=latest\n        :target: https://pydftools.readthedocs.io/en/latest/?badge=latest\n        :alt: Documentation Status\n\n\nA pure-python port of the ``dftools`` R package.\n\nThis package attempts to imitate the ``dftools`` package (repo: https://github.com/obreschkow/dftools ) quite closely,\nwhile being as Pythonic as possible. Do note that 2D+ models are not yet implemented in this Python port, and neither\nare non-parametric models. Hopefully they will be along soon.\n\nFrom ``dftool``'s description:\n\n    This package can find the most likely P parameters of a D-dimensional distribution function (DF) generating\n    N objects, where each object is specified by D observables with measurement uncertainties. For instance, if the objects\n    are galaxies, it can fit a MF (P=1), a mass-size distribution (P=2) or the mass-spin-morphology distribution (P=3).\n    Unlike most common fitting approaches, this method accurately accounts for measurement is uncertainties and complex\n    selection functions. A full description of the algorithm can be found in Obreschkow et al. (2017).\n\nIn short, clean out Eddington bias from your fits:\n\n.. image:: https://user-images.githubusercontent.com/1272030/31757852-60cb6ebc-b4dd-11e7-8ce9-32b3232e8f94.png\n   :scale: 30 %\n\n* Free software: MIT license\n* Documentation: https://pydftools.readthedocs.io.\n\n\nFeatures\n--------\n\n* Simple and fast parameter fitting for generative distribution functions\n* Several examples (with astronomical applications in mind)\n* Several plotting routines so that you can go from nothing to a plot in minutes\n* A ``mockdata()`` function which can produce data to fit.\n* Support for arbitrary 1D models, several kinds of selection functions, jackknife and bootstrap resampling, Gaussian\n  error estimation and more.\n\nCredits\n---------\n\nThis package was created with Cookiecutter_ and the `audreyr/cookiecutter-pypackage`_ project template.\n\n.. _Cookiecutter: https://github.com/audreyr/cookiecutter\n.. _`audreyr/cookiecutter-pypackage`: https://github.com/audreyr/cookiecutter-pypackage\n\n\n\n=======\nHistory\n=======\n\n0.1.0 (2017-10-25)\n------------------\n\n* First release on PyPI.\n* All basic examples working as expected\n* TravisCI, Readthedocs set up.\n* Does not have multi-dimension support, or non-parametric support.\n",
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