Metadata-Version: 1.1
Name: scikit-posthocs
Version: 0.6.6
Summary: Statistical post-hoc analysis and outlier detection algorithms
Home-page: http://github.com/maximtrp/scikit-posthocs
Author: Maksim Terpilowski
Author-email: maximtrp@gmail.com
License: MIT
Description: scikit-posthocs
        ===============
        
        **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.
        
        
        Background
        ----------
        
        Python statistical ecosystem comprises multiple packages. However, it still has
        numerous gaps and is surpassed by R packages and capabilities.
        
        `SciPy <https://www.scipy.org/>`_ (version 1.2.0) offers *Student*, *Wilcoxon*,
        and *Mann-Whitney* tests that are not adapted to multiple pairwise comparisons.
        `Statsmodels <http://statsmodels.sourceforge.net/>`_ (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 <https://rdrr.io/cran/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.
        
        
        Features
        --------
        
        - *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 adjustments for multiple pairwise
        comparisons.
        
        Dependencies
        ------------
        
        - `NumPy and SciPy packages <https://www.scipy.org/>`_
        - `Statsmodels <http://statsmodels.sourceforge.net/>`_
        - `Pandas <http://pandas.pydata.org/>`_
        - `Matplotlib <https://matplotlib.org/>`_
        - `Seaborn <https://seaborn.pydata.org/>`_
        
        Compatibility
        -------------
        
        Package is compatible with Python 2 and Python 3.
        
        Install
        -------
        
        You can install the package using ``pip`` :
        
        .. code:: bash
        
          $ pip install scikit-posthocs
        
Keywords: statistics posthoc anova
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Information Technology
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Classifier: Topic :: Scientific/Engineering :: Mathematics
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
