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        "author": "Wojciech Ruszczewski",
        "author_email": "scipy@wr.waw.pl",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 4 - Beta",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3.5",
            "Topic :: Scientific/Engineering :: Information Analysis"
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        "description": "==========\nscikit-gof\n==========\n\nProvides variants of Kolmogorov-Smirnov, Cramer-von Mises and Anderson-Darling\ngoodness of fit tests for fully specified continuous distributions.\n\nExample\n=======\n\n.. code:: python\n\n    >>> from scipy.stats import norm, uniform\n    >>> from skgof import ks_test, cvm_test, ad_test\n\n    >>> ks_test((1, 2, 3), uniform(0, 4))\n    GofResult(statistic=0.25, pvalue=0.97...)\n\n    >>> cvm_test((1, 2, 3), uniform(0, 4))\n    GofResult(statistic=0.04..., pvalue=0.95...)\n\n    >>> data = norm(0, 1).rvs(random_state=1, size=100)\n    >>> ad_test(data, norm(0, 1))\n    GofResult(statistic=0.75..., pvalue=0.51...)\n    >>> ad_test(data, norm(.3, 1))\n    GofResult(statistic=3.52..., pvalue=0.01...)\n\nSimple tests\n============\n\nScikit-gof currently only offers three nonparametric tests that let you\ncompare a sample with a reference probability distribution. These are:\n\n``ks_test()``\n    Kolmogorov-Smirnov supremum statistic; almost the same as\n    ``scipy.stats.kstest()`` with ``alternative='two-sided'`` but with\n    (hopefully) somewhat more precise p-value calculation;\n\n``cvm_test()``\n    Cramer-von Mises L2 statistic, with a rather crude estimation of the\n    statistic distribution (but seemingly the best available);\n\n``ad_test()``\n    Anderson-Darling statistic with a fair approximation of its distribution;\n    unlike the composite ``scipy.stats.anderson()`` this one needs a fully\n    specified hypothesized distribution.\n\nSimple test functions use a common interface, taking as the first argument the\ndata (sample) to be compared and as the second argument a frozen ``scipy.stats``\ndistribution.\nThey return a named tuple with two fields: ``statistic`` and ``pvalue``.\n\nFor a simple example consider the hypothesis that the sample (.4, .1, .7) comes\nfrom the uniform distribution on [0, 1]:\n\n.. code:: python\n\n    if ks_test((.4, .1, .7), unif(0, 1)).pvalue < .05:\n        print(\"Hypothesis rejected with 5% significance.\")\n\nIf your samples are very large and you have them sorted ahead of time, pass\n``assume_sorted=True`` to save some time that would be wasted resorting.\n\nExtending\n=========\n\nSimple tests are composed of two phases: calculating the test statistic and\ndetermining how likely is the resulting value (under the hypothesis).\nNew tests may be defined by providing a new statistic calculation routine or an\nalternative distribution for a statistic.\n\nFunctions calculating statistics are given evaluations of the reference\ncumulative distribution function on sorted data and are expected to return\na single number.\nFor a simple test, if the sample indeed comes from the hypothesized (continuous)\ndistribution, the values passed to the function should be uniformly distributed\nover [0, 1].\n\nHere is a simplistic example of how a statistic function might look like:\n\n.. code:: python\n\n    def ex_stat(data):\n        return abs(data.sum() - data.size / 2)\n\nStatistic functions for the provided tests, ``ks_stat()``, ``cvm_stat()``,\nand ``ad_stat()``, can be imported from ``skgof.ecdfgof``.\n\nStatistic distributions should derive from ``rv_continuous`` and implement\nat least one of the abstract ``_cdf()`` or ``_pdf()`` methods (you might\nalso consider directly coding ``_sf()`` for increased precision of results\nclose to 1). For example:\n\n.. code:: python\n\n    from numpy import sqrt\n    from scipy.stats import norm, rv_continuous\n\n    class ex_unif_gen(rv_continuous):\n        def _cdf(self, statistic, samples):\n            return 1 - 2 * norm.cdf(-statistic, scale=sqrt(samples / 12))\n\n    ex_unif = ex_unif_gen(a=0, name='ex-unif', shapes='samples')\n\nThe provided distributions live in separate modules, respectively ``ksdist``,\n``cvmdist``, and ``addist``.\n\nOnce you have a statistic calculation function and a statistic distribution the\ntwo parts can be combined using ``simple_test``:\n\n.. code:: python\n\n    from functools import partial\n    from skgof.ecdfgof import simple_test\n\n    ex_test = partial(simple_test, stat=ex_stat, pdist=ex_unif)\n\n**Exercise**: The example test has a fundamental flaw. Can you point it out?\n\n..  The test is not consistent under all alternatives. For instance, if the\n    hypothesis was that samples come from the uniform distribution on [0, 1],\n    but they really were \"drawn\" from the degenerate distribution at .5, the\n    test would never notice, even for arbitrarily large sample sizes.\n\n    Moreover, the asymptotic distribution is not a good approximation of the\n    actual statistic distribution for small sample sizes.\n\nInstallation\n============\n\n.. code:: bash\n\n    pip install scikit-gof\n\nRequires recent versions of Python (> 3), NumPy (>= 1.10) and SciPy.\n\nPlease fix or point out any errors, inaccuracies or typos you notice.",
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