MissMecha Python Package Official Release!
We are excited to officially announce the first public release of the MissMecha Python package! 🎉
MissMecha is an open-source toolkit for simulating, analyzing, and evaluating missing data mechanisms (MCAR, MAR, MNAR) in Python. It is designed to help data scientists, researchers, and educators better understand how missingness impacts their data and models.
Key features include:
- ✅ Flexible missing data generation (MCAR, MAR, MNAR)
- ✅ Easy-to-use
MissMechaGeneratorwith sklearn-style API - ✅ Built-in evaluation metrics for imputation quality
- ✅ Visualization tools for missingness patterns
- ✅ Comprehensive documentation and examples
You can explore the package on PyPI, or check out the official documentation.
pip install missmecha-py
Why MissMecha?
Handling missing data is critical in real-world applications, but it’s often oversimplified. MissMecha makes it easy to generate controlled missingness patterns for benchmarking, evaluate imputation methods, and gain insights into data reliability.
We invite you to try MissMecha, provide feedback, and contribute to its ongoing development. Let’s make missing data research more transparent, reproducible, and accessible for all.
Thank you for your support!