estyp 0.4.1

Extended Statistical Toolkit Yet Practical

ESTYP: Extended Statistical Toolkit Yet Practical

Description

This library is a collection of statistical functions for Python.

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

Changelog

V0.4.1

• Bug fixes in linear_model.LogisticRegression() class.
• Added downloads badge to README.
• Changed sklearn>=1.2.1 to scipy>=1.3.0 as a depedency of the library.

V0.4.0

• Added testing.prop_test() function to perform a test of proportions.
• Added testing.CheckModel() class to perform linear regression assumptions checking.
• Added badges to README.
• Minor changes in README.

V0.3.0

• Changed scipy>=1.11.1 to scipy>=1.10.1 as a depedency of the library.
• New modularization of the functions in the linear_model module.
• Added linear_model.stepwise.forward_selection() function to perform forward variable selection based in p-values.
• Added testing.nested_models_test() function to perform nested models testing.
• Added option to specity aditional parameters of the model like kwargs in linear_model.stepwise.forward_selection() and linear_model.stepwise.both_selection() functions.
• Minor changes in README.

V0.2.5

• Added scipy>=1.11.1 as a depedency of the library.
• New modularization of the functions in the testing module.
• R like documentation in the testing.var_test() function.
• Added testing.t_test() function to perform t-test like in software R.

Features

• 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.
• 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.nested_models_test(): Performs a nested models test to compare two nested models using deviance criterion.

Installation

To install this library, you can use PyPI:

pip install estyp

License

This library is under the MIT license.

Contact

If you have any questions about this library, you can contact me at [e-mail].