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Introduction

Background

Python statistical ecosystem is comprised of multiple packages. However, it still has numerous gaps and is surpassed by R packages and capabilities.

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.

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.

Features

image

  • Omnibox tests:
    • Durbin test (for balanced incomplete block design).
  • Parametric pairwise multiple comparisons tests:
    • Scheffe test.
    • Student T test.
    • Tamhane T2 test.
    • TukeyHSD test.
  • Non-parametric tests for factorial design:
    • Conover test.
    • Dunn test.
    • Dwass, Steel, Critchlow, and Fligner test.
    • Mann-Whitney test.
    • Nashimoto and Wright (NPM) test.
    • Nemenyi test.
    • van Waerden test.
    • Wilcoxon test.
  • Non-parametric tests for block design:
    • Conover test.
    • Durbin and Conover test.
    • Miller test.
    • Nemenyi test.
    • Quade test.
    • Siegel test.
  • Other tests:
    • Anderson-Darling test.
    • Mack-Wolfe test.
    • Hayter (OSRT) test.
  • Outliers detection tests:
    • Simple test based on interquartile range (IQR).
    • Grubbs test.
    • Tietjen-Moore test.
    • Generalized Extreme Studentized Deviate test (ESD test).
  • Plotting functionality (e.g. significance plots).

All post hoc tests are capable of p value adjustments for multiple pairwise comparisons.