Metadata-Version: 1.1
Name: scikit-gof
Version: 0.0.2
Summary: Variations on goodness of fit tests for SciPy.
Home-page: http://github.org/wrwrwr/scikit-gof
Author: Wojciech Ruszczewski
Author-email: scipy@wr.waw.pl
License: MIT
Description: ==========
        scikit-gof
        ==========
        
        Provides variants of Kolmogorov-Smirnov, Cramer-von Mises and Anderson-Darling
        goodness of fit tests for fully specified continuous distributions.
        
        The Kolmogorov-Smirnov statistic distribution is (hopefully) somewhat more
        precise compared to what SciPy has to offer at the time of writing.
        
        Example
        =======
        
        .. code:: python
        
            >>> from scipy.stats import norm, uniform
            >>> from skgof import ks_test, cvm_test, ad_test
        
            >>> ks_test((1, 2, 3), uniform(0, 4))
            GofResult(statistic=0.25, pvalue=0.97...)
        
            >>> cvm_test((1, 2, 3), uniform(0, 4))
            GofResult(statistic=0.04..., pvalue=0.95...)
        
            >>> data = norm(0, 1).rvs(random_state=1, size=100)
            >>> ad_test(data, norm(0, 1))
            GofResult(statistic=0.75..., pvalue=0.51...)
            >>> ad_test(data, norm(.3, 1))
            GofResult(statistic=3.52..., pvalue=0.01...)
        
        Installation
        ============
        
        .. code:: bash
        
            pip install scikit-gof
        
        Requires recent versions of Python (> 3), NumPy (>= 1.10) and SciPy.
        
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3.4
