{ "info": { "author": "Maksim Terpilowski", "author_email": "maximtrp@gmail.com", "bugtrack_url": null, "classifiers": [ "Development Status :: 5 - Production/Stable", "Intended Audience :: Education", "Intended Audience :: Information Technology", "Intended Audience :: Science/Research", "License :: OSI Approved :: BSD License", "Programming Language :: Python :: 2", "Programming Language :: Python :: 2.6", "Programming Language :: Python :: 2.7", "Programming Language :: Python :: 3", "Programming Language :: Python :: 3.1", "Programming Language :: Python :: 3.2", "Programming Language :: Python :: 3.3", "Programming Language :: Python :: 3.4", "Programming Language :: Python :: 3.5", "Programming Language :: Python :: 3.6", "Programming Language :: Python :: 3.7", "Topic :: Scientific/Engineering :: Information Analysis", "Topic :: Scientific/Engineering :: Mathematics" ], "description": "scikit-posthocs\n===============\n\n**scikit-posthocs** is a Python package which provides post hoc tests for pairwise multiple comparisons that are usually performed in statistical data analysis to assess the differences between group levels if a statistically significant result of ANOVA test has been obtained.\n\n**scikit-posthocs** is tightly integrated with Pandas DataFrames and NumPy arrays to ensure fast computations and convenient data import and storage.\n\nThis package will be useful for statisticians, data analysts, and researchers who use Python in their work.\n\n\nBackground\n----------\n\nPython statistical ecosystem is comprised of multiple packages. However, it still has numerous gaps and is surpassed by R packages and capabilities.\n\n`SciPy `_ (version 1.2.0) offers *Student*, *Wilcoxon*, and *Mann-Whitney* tests which are not adapted to multiple pairwise comparisons. `Statsmodels `_ (version 0.9.0) features *TukeyHSD* test which needs some extra actions to be fluently integrated into a data analysis pipeline. `Statsmodels `_ also has good helper methods: ``allpairtest`` (adapts an external function such as ``scipy.stats.ttest_ind`` to multiple pairwise comparisons) and ``multipletests`` (adjusts *p* values to minimize type I and II errors). `PMCMRplus `_ is a very good R package which has no rivals in Python as it offers more than 40 various tests (including post hoc tests) for factorial and block design data. PMCMRplus was an inspiration and a reference for *scikit-posthocs*.\n\n*scikit-posthocs* attempts to improve Python statistical capabilities by offering a lot of parametric and nonparametric post hoc tests along with outliers detection and basic plotting methods.\n\n\nFeatures\n--------\n\n- *Parametric* pairwise multiple comparisons tests:\n\n - Scheffe test.\n - Student T test.\n - Tamhane T2 test.\n - TukeyHSD test.\n\n- *Non-parametric* tests for factorial design:\n\n - Conover test.\n - Dunn test.\n - Dwass, Steel, Critchlow, and Fligner test.\n - Mann-Whitney test.\n - Nashimoto and Wright (NPM) test.\n - Nemenyi test.\n - van Waerden test.\n - Wilcoxon test.\n\n- *Non-parametric* tests for block design:\n\n - Conover test.\n - Durbin and Conover test.\n - Miller test.\n - Nemenyi test.\n - Quade test.\n - Siegel test.\n\n- Other tests:\n\n - Anderson-Darling test.\n - Mack-Wolfe test.\n - Hayter (OSRT) test.\n\n- Outliers detection tests:\n\n - Simple test based on interquartile 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