Metadata-Version: 2.1
Name: estyp
Version: 0.5.1
Summary: Extended Statistical Toolkit Yet Practical
Project-URL: Homepage, https://github.com/estebanrucan/estyp
Project-URL: Bug Tracker, https://github.com/estebanrucan/estyp/issues
Author-email: estebanrucan <errucan@gmail.com>
License: MIT License
        
        Copyright (c) 2023 Esteban Rucán Carrasco
        
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License-File: LICENSE
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Requires-Python: >=3.9.12
Requires-Dist: kmodes>=0.12.2
Requires-Dist: matplotlib>=3.4.3
Requires-Dist: numpy>=1.22.3
Requires-Dist: patsy>=0.5.3
Requires-Dist: scikit-learn>=1.3.0
Requires-Dist: scipy>=1.10.1
Requires-Dist: statsmodels>=0.13.5
Requires-Dist: tqdm>=4.65.0
Description-Content-Type: text/markdown

# ESTYP: Extended Statistical Toolkit Yet Practical

[![Downloads](https://static.pepy.tech/badge/estyp)](https://pepy.tech/project/estyp) [![Documentation Status](https://readthedocs.org/projects/estyp/badge/?version=latest)](https://estyp.readthedocs.io/en/latest/?badge=latest) [![PyPI version](https://badge.fury.io/py/estyp.svg)](https://badge.fury.io/py/estyp) [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT) [![Contributions](https://img.shields.io/badge/Contributions-welcome-blue.svg)](https://github.com/estebanrucan/estyp/issues) [![Chilean](https://img.shields.io/badge/Made_in-%F0%9F%87%A8%F0%9F%87%B1_Chile-blue.svg)](https://es.wikipedia.org/wiki/Chile)

## Description

ESTYP (Extended Statistical Toolkit Yet Practical) is a Python library that serves as a multifaceted toolkit for statistical analysis. The `testing` module encompasses a wide range of statistical tests, including t-tests, chi-squared tests, and correlation tests, providing robust methods for data comparison and validation. In the `linear_model` module, users can find functionalities related to logistic regression, including variable selection techniques and additional methods for calculating confidence intervals and p-values. This module enhances the capabilities of traditional logistic regression analysis. The cluster module is designed to assist in clustering analysis, offering tools to identify the optimal number of `clusters` using methods like the elbow or silhouette techniques. Together, these modules form a comprehensive and practical statistical toolkit that caters to various analytical needs. 

Actually, the name comes from the way my friends call me (Esti), plus "p" which is the initial of `python`.

## Installation

To install this library, you can use PyPI:

```bash
pip install estyp
```

## Documentation

You can have a friendly introduction to this library in the [documentation](https://estyp.readthedocs.io/en/latest/).

## Changelog

You can see the changelog [here](./CHANGELOG.md)

## Features

### `testing` module

* `testing.CheckModel()`: This class provides methods to test the assumptions of the linear regression model., inspired by the `performance::check_model()` function of the R software.
* `testing.t_test()`: Performs one and two sample t-tests on groups of data. This function is inspired by the `t.test()` function of the R software.
* `testing.var_test()`: Performs an F test to compare the variances of two samples from normal populations. This function is inspired by the `var.test()` function of the R software.
* `testing.prop_test()`: it can be used for testing the null that the proportions (probabilities of success) in several groups are the same, or that they equal certain given values. This function is inspired by the `prop.test()` function of the R software.
* `testing.chisq_test()`: Performs a chi-squared test of independence of variables in a contingency table. This function is inspired by the `chisq.test()` function of the R software.
* `testing.cor_test()`: Performs a correlation test with Pearson, Spearman or Kendall method. This function is inspired by the `cor.test()` function of the R software.
* `testing.nested_models_test()`: Performs a nested models test to compare two nested models using deviance criterion.

### `linear_model` module

* `linear_model.LogisticRegression()`: This class implements a logistic regression model. It inherits from the `LogisticRegression()` class from `scikit-learn`, but adds additional methods for calculating confidence intervals, p-values, and model summaries like `Logit` class in `statsmodels`.
* `linear_model.stepwise.both_selection()`: This function performs both forward and backward variable selection using the Akaike Information Criterion (AIC). 
* `linear_model.stepwise.forward_selection()`: This function performs forward variable selection based on p-values.

### `cluster` module

* `cluster.NClusterSearch`: A helper class to identify the optimal number of clusters for clustering algorithms with elbow or silhuette methods.


## License

This library is under the MIT license.

## Contact

If you have any questions about this library, you can contact me at [LinkedIn](https://www.linkedin.com/in/estebanrucan/).