Metadata-Version: 2.1
Name: hypothesize
Version: 1.1.0
Summary: A Python package for comparing groups and measuring associations using robust statistics.
Home-page: https://github.com/Alcampopiano/hypothesize
Author: Allan Campopiano
Author-email: campopianoa@hcdsb.org
License: BSD 3-clause
Description: # Hypothesize <a href="https://Alcampopiano.github.io/hypothesize/"><img align="right" src="https://github.com/Alcampopiano/hypothesize/blob/master/docs/docs/img/vp_inv.png" height="50"></img></a>
        [![status](https://joss.theoj.org/papers/caf4095b3cdcc3adbb0252c995d59926/status.svg)](https://joss.theoj.org/papers/caf4095b3cdcc3adbb0252c995d59926)
        ![tests](https://github.com/Alcampopiano/hypothesize/workflows/tests/badge.svg)
        [![PyPI version](https://img.shields.io/pypi/v/hypothesize?style=flat-square)](https://pypi.org/project/hypothesize/)
        [![PyPI - Downloads](https://img.shields.io/pypi/dw/hypothesize?style=flat-square)](https://pypistats.org/packages/hypothesize)
        [![license](https://img.shields.io/pypi/l/hypothesize?style=flat-square)](https://github.com/Alcampopiano/hypothesize/blob/master/LICENSE)
        [![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Alcampopiano/hypothesize/blob/master/examples/hypothesize_notebook_for_colab.ipynb)
        
        A Python package for hypothesis testing using robust statistics.
        
        :book: Please visit the [Hypothesize documentation site](https://Alcampopiano.github.io/hypothesize/) for more details.
        
        ## Basic Example
        ### A robust measure of association using winsorized correlation
        
        ```python
        from hypothesize.measuring_associations import wincor
        from hypothesize.utilities import create_example_data
        
        # creating an example DataFrame with columns "cell_1" and "cell_2"
        df=create_example_data(2)
        
        results=wincor(df.cell_1, df.cell_2)
        
        # returning the correlation, number of observations, p-value, and winsorized covariance
        print(results)
        {'cor': 0.11, 'nval': 50, 'sig': 0.44, 'wcov': 0.01}
        ```
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Environment :: Console
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: BSD License
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Requires-Python: >=3.6
Description-Content-Type: text/markdown
