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https://github.com/aschleg/hypothetical
Hypothesis and statistical testing in Python
https://github.com/aschleg/hypothetical
analysis comparison-tests frequentist-methods frequentist-statistics hypothesis hypothesis-testing inferential-statistics nonparametric-statistics nonparametric-tests python statistical-inference statistical-tests statistics statistics-library
Last synced: 3 months ago
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Hypothesis and statistical testing in Python
- Host: GitHub
- URL: https://github.com/aschleg/hypothetical
- Owner: aschleg
- License: mit
- Created: 2018-01-22T05:29:05.000Z (about 7 years ago)
- Default Branch: master
- Last Pushed: 2020-08-20T20:06:17.000Z (over 4 years ago)
- Last Synced: 2024-11-12T12:49:59.171Z (3 months ago)
- Topics: analysis, comparison-tests, frequentist-methods, frequentist-statistics, hypothesis, hypothesis-testing, inferential-statistics, nonparametric-statistics, nonparametric-tests, python, statistical-inference, statistical-tests, statistics, statistics-library
- Language: Python
- Homepage:
- Size: 853 KB
- Stars: 64
- Watchers: 4
- Forks: 10
- Open Issues: 5
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- License: LICENSE.txt
Awesome Lists containing this project
README
# hypothetical - Hypothesis and Statistical Testing in Python
[](https://travis-ci.org/aschleg/hypothetical)
[](https://ci.appveyor.com/project/aschleg/hypy)
[](https://coveralls.io/github/aschleg/hypothetical?branch=master)
[](https://www.codacy.com/app/aschleg/hypothetical?utm_source=github.com&utm_medium=referral&utm_content=aschleg/hypothetical&utm_campaign=Badge_Grade)
[](https://libraries.io/github/aschleg/hypothetical)
Python library for conducting hypothesis and other group comparison tests.
## Installation
The best way to install `hypothetical` is through `pip`.
```bash
pip install hypothetical
```
For those interested, the most recent development version of the library can also be installed by cloning or
downloading the repo.
~~~ bash
git clone [email protected]:aschleg/hypothetical.git
cd hypothetical
python setup.py install
~~~
## Available Methods
### Analysis of Variance
* One-way Analysis of Variance (ANOVA)
* One-way Multivariate Analysis of Variance (MANOVA)
* Bartlett's Test for Homogenity of Variances
* Levene's Test for Homogenity of Variances
* Van Der Waerden's (normal scores) Test
### Contingency Tables and Related Tests
* Chi-square test of independence
* Fisher's Exact Test
* McNemar's Test of paired nominal data
* Cochran's Q test
* D critical value (used in the Kolomogorov-Smirnov Goodness-of-Fit test).
### Critical Value Tables and Lookup Functions
* Chi-square statistic
* r (one-sample runs test and Wald-Wolfowitz runs test) statistic
* Mann-Whitney U-statistic
* Wilcoxon Rank Sum W-statistic
### Descriptive Statistics
* Kurtosis
* Skewness
* Mean Absolute Deviation
* Pearson Correlation
* Spearman Correlation
* Covariance
- Several algorithms for computing the covariance and covariance matrix of
sample data are available
* Variance
- Several algorithms are also available for computing variance.
* Simulation of Correlation Matrices
- Multiple simulation algorithms are available for generating correlation matrices.
### Factor Analysis
* Several algorithms for performing Factor Analysis are available, including principal components, principal
factors, and iterated principal factors.
### Hypothesis Testing
* Binomial Test
* t-test
- paired, one and two sample testing
### Nonparametric Methods
* Friedman's test for repeated measures
* Kruskal-Wallis (nonparametric equivalent of one-way ANOVA)
* Mann-Whitney (two sample nonparametric variant of t-test)
* Mood's Median test
* One-sample Runs Test
* Wald-Wolfowitz Two-Sample Runs Test
* Sign test of consistent differences between observation pairs
* Wald-Wolfowitz Two-Sample Runs test
* Wilcoxon Rank Sum Test (one sample nonparametric variant of paired and one-sample t-test)
### Normality and Goodness-of-Fit Tests
* Chi-square one-sample goodness-of-fit
* Jarque-Bera test
### Post-Hoc Analysis
* Tukey's Honestly Significant Difference (HSD)
* Games-Howell (nonparametric)
### Helpful Functions
* Add noise to a correlation or other matrix
* Tie Correction for ranked variables
* Contingency table marginal sums
* Contingency table expected frequencies
* Runs and count of runs
## Goal
The goal of the `hypothetical` library is to help bridge the gap in statistics and hypothesis testing
capabilities of Python closer to that of R. Python has absolutely come a long way with several popular and
amazing libraries that contain a myriad of statistics functions and methods, such as [`numpy`](http://www.numpy.org/),
[`pandas`](https://pandas.pydata.org/), and [`scipy`](https://www.scipy.org/); however, it is my humble opinion that
there is still more that can be done to make Python an even better language for data and statistics computation. Thus,
it is my hope with the `hypothetical` library to build on top of the wonderful Python packages listed earlier and
create an easy-to-use, feature complete, statistics library. At the end of the day, if the library helps a user
learn more about statistics or get the information they need in an easy way, then I consider that all the success
I need!
## Requirements
* Python 3.5+
* `numpy>=1.13.0`
* `numpy_indexed>=0.3.5`
* `pandas>=0.22.0`
* `scipy>=1.1.0`
* `statsmodels>=0.9.0`
## License
MIT