Variations on goodness of fit tests for SciPy.
Project description
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
>>> 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
pip install scikit-gof
Requires recent versions of Python (> 3), NumPy (>= 1.10) and SciPy.
Project details
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scikit-gof-0.0.2.tar.gz
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