Metadata-Version: 1.1
Name: scikit-posthocs
Version: 0.3.4
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: BSD
Description-Content-Type: UNKNOWN
Description: ===============
        scikit-posthocs
        ===============
        
        .. image:: https://travis-ci.org/maximtrp/scikit-posthocs.svg?branch=master
            :target: https://travis-ci.org/maximtrp/scikit-posthocs
        .. image:: https://img.shields.io/github/issues/maximtrp/scikit-posthocs.svg
            :target: https://github.com/maximtrp/scikit-posthocs/issues
        .. image:: https://img.shields.io/pypi/v/scikit-posthocs.svg
            :target: https://pypi.python.org/pypi/scikit-posthocs/
        
        This Python package provides statistical post-hoc tests for pairwise multiple comparisons
        and outlier detection algorithms.
        
        Features
        --------
        
        - Multiple comparisons parametric and nonparametric tests (some are ported from R's
          `PMCMR <https://cran.r-project.org/web/packages/PMCMR/index.html>`_ package):
        
          - Conover, Dunn, and Nemenyi tests for use with Kruskal-Wallis test.
          - Quade, van Waerden, and Durbin tests.
          - Conover and Nemenyi tests for use with Friedman test.
          - Student, Mann-Whitney, Wilcoxon, and TukeyHSD tests.
        
          All tests are capable of p adjustments for multiple pairwise comparisons.
        
        - Plotting functionality (e.g. significance plots).
        
        - Outlier detection algorithms:
        
          - Simple test based on interquartile range (IQR).
          - Grubbs test.
          - Tietjen-Moore test.
          - Generalized Extreme Studentized Deviate test (ESD test).
        
        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 with:
        ``pip install scikit-posthocs``
        
        Example
        -------
        
          >>> import scikit_posthocs as sp
          >>> x = [[1,2,3,5,1], [12,31,54], [10,12,6,74,11]]
          >>> # This will return a symmetric array of p values
          >>> sp.posthoc_conover(x, p_adjust = 'holm')
          array([[-1.        ,  0.00119517,  0.00278329],
                 [ 0.00119517, -1.        ,  0.18672227],
                 [ 0.00278329,  0.18672227, -1.        ]])
        
        Credits
        -------
        
        Thorsten Pohlert, PMCMR author and maintainer
        
Keywords: statistics posthoc anova
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
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 :: BSD License
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.6
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.1
Classifier: Programming Language :: Python :: 3.2
Classifier: Programming Language :: Python :: 3.3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
