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: about 2 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 (almost 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 (about 2 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

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README

        
# hypothetical - Hypothesis and Statistical Testing in Python

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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