Skip to main content

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


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for scikit-gof, version 0.0.1
Filename, size File type Python version Upload date Hashes
Filename, size scikit-gof-0.0.1.tar.gz (8.2 kB) File type Source Python version None Upload date Hashes View

Supported by

Pingdom Pingdom Monitoring Google Google Object Storage and Download Analytics Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN DigiCert DigiCert EV certificate StatusPage StatusPage Status page