This repository contains a collection of Python scripts and modules for performing various statistical tests. These tests are designed to help you analyze data, make inferences, and draw conclusions based on statistical methods. The repository is organized to be easy to use, modular, and extensible.
To use this repository, you need to have Python 3.9 or later installed on your system. You can install the required dependencies by following these steps:
Clone the repository:
git clone https://github.com/PySATL/pysatl-criterion.gitInstall dependencies:
poetry installYou're all set! You can now import and use the statistical tests in your Python scripts.
# import needed criterion from pysatl_criterion
from pysatl_criterion.normal import KolmogorovSmirnovNormalityGofStatistic
# make a criterion object
criterion = KolmogorovSmirnovNormalityGofStatistic(mean=0, var=1)
# initialize test data
x = [0.1, 0.7, 0.5, 0.3]
# then run algorithm
statistic = criterion.execute_statistic(x)
# print the results
print(f"Statistic result: {statistic}")
# output:
# Statistic result: 0.539827837277029We invite you to read the bot documentation to ensure you understand how the PySATL Criterion lib is working.
Please find the complete documentation on the PySATL Criterion website.
If you discover a bug in the PySATL criterion lib, please search the issue tracker first. If it hasn't been reported, please create a new issue and ensure you follow the template guide so that the team can assist you as quickly as possible.
For every issue created, kindly follow up and mark satisfaction or reminder to close issue when equilibrium ground is reached.
--Maintain github's community policy--
Have you a great idea to improve the bot you want to share? Please, first search if this feature was not already discussed. If it hasn't been requested, please create a new request and ensure you follow the template guide so that it does not get lost in the bug reports.
Feel like the PySATL criterion lib is missing a feature? We welcome your pull requests!
Please read the Contributing document to understand the requirements before sending your pull-requests.
Coding is not a necessity to contribute - maybe start with improving the documentation? Issues labeled good first issue can be good first contributions, and will help get you familiar with the codebase.
Note before starting any major new feature work, please open an issue describing what you are planning to do. This will ensure that interested parties can give valuable feedback on the feature, and let others know that you are working on it.
Important: Always create your PR against the develop branch, not stable.
- Python >= 3.9
- poetry
- git
- virtualenv (Recommended)
This project is licensed under the terms of the MIT license. See the LICENSE for more information.