v0.0.87

README.md

@@ -2,7 +2,7 @@
 
 [![License](https://img.shields.io/badge/License-MIT-blue.svg)](LICENSE)
 [![Documentation](https://img.shields.io/badge/Documentation-Link-green.svg)](docs/)
-[![Version](https://img.shields.io/badge/Version-0.0.86-brightgreen.svg)](https://github.com/ArthurMangussi/pymdatagen/releases/tag/v0.0.86)
+[![Version](https://img.shields.io/badge/Version-0.0.87-brightgreen.svg)](https://github.com/ArthurMangussi/pymdatagen/releases/tag/v0.0.87)
 
 This package has been developed to address a gap in machine learning research, specifically the artificial generation of missing data. Santos et al. (2019) provided a survey that presents various strategies for both univariate and multivariate scenarios, but the Python community still needs implementations of these strategies. Besides, Pereira et al. (2023) proposed new benchmark strategies for Missing Not At Random (MNAR), and these novel methods also need to be implemented in Python. Hence, **missing-data-generator** (mdatagen) is a Python package that implements methods for generating missing values ​​for data, including Missing At Random (MAR), Missing Not At Random (MNAR), and Missing Completly At Random (MCAR) mechanisms in both univariate and multivariate scenarios.
 

setup.py

@@ -7,7 +7,7 @@
 
 setup(
     name='mdatagen',
-    version='0.0.86',
+    version='0.0.87',
     keywords=['machine learning', 'preprocessing data'],
     license='MIT',
     author='Arthur Dantas Mangussi',