Statistical post-hoc analysis and outlier detection algorithms
scikit-posthocs is a Python package that 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.
scikit-posthocs is tightly integrated with Pandas DataFrames and NumPy arrays to ensure fast computations and convenient data import and storage.
This package will be useful for statisticians, data analysts, and researchers who use Python in their work.
Python statistical ecosystem comprises 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 that are not adapted to multiple pairwise comparisons. Statsmodels (version 0.9.0) features TukeyHSD test that 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 that 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.
Parametric pairwise multiple comparisons tests:
Student T test.
Tamhane T2 test.
Non-parametric tests for factorial design:
Dwass, Steel, Critchlow, and Fligner test.
Nashimoto and Wright (NPM) test.
van Waerden test.
Non-parametric tests for block design:
Durbin and Conover test.
Hayter (OSRT) test.
Outliers detection tests:
Simple test based on interquartile range (IQR).
Generalized Extreme Studentized Deviate test (ESD test).
Plotting functionality (e.g. significance plots).
All post hoc tests are capable of p adjustments for multiple pairwise comparisons.
Package is compatible with Python 2 and Python 3.
You can install the package using pip :
$ pip install scikit-posthocs
Release history Release notifications | RSS feed
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Hashes for scikit_posthocs-0.8.0-py3-none-any.whl