| Name | scikit-posthocs JSON |
| Version |
0.9.0
JSON |
| download |
| home_page | |
| Summary | Statistical post-hoc analysis and outlier detection algorithms |
| upload_time | 2024-02-18 18:57:34 |
| maintainer | |
| docs_url | None |
| author | |
| requires_python | >=3.9 |
| license | Copyright (c) 2020 Maksim Terpilowski Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. |
| keywords |
statistics
stats
posthoc
anova
data science
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
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|
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
Raw data
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