missmecha-py 0.1.0

All about missing data mechanisms: simulation, analysis, and visualization

🧩 MissMecha

MissMecha is a Python package dedicated to the systematic simulation, visualization, and evaluation of missing data mechanisms. Our goal is to provide a unified interface for generating, inspecting, and analyzing missingness — supporting research, benchmarking, and education.

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Highlights

• 🔍 All About Missing Mechanisms

  • Simulate MCAR, MAR, and MNAR in flexible formats
  • Currently supports:
    • 3× MCAR strategies
    • 8× MAR strategies
    • 6× MNAR strategies
    • Experimental support for categorical and time series missingness

• Missingness Pattern Visualization

  • Visual tools to observe missing patterns
  • Helps identify potential mechanism types (MCAR vs MAR vs MNAR)

• Flexible Generator Interface

  • Column-wise or global missingness simulation
  • Sklearn-style fit / transform methods
  • Supports custom rates, dependencies, labels, and logic

• Evaluation Toolkit

  • Quantitative metrics including MSE, MAE, RMSE, and AvgERR
  • Built-in support for Little’s MCAR test

• SimpleSmartImputer

  • Lightweight imputer that automatically detects numerical and categorical columns
  • Applies mean or mode imputation with verbose diagnostics

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Motivation

Working with missing data often involves disparate implementations and inconsistent assumptions across studies.
MissMecha brings together widely used missing data mechanisms into a single, structured, and reproducible Python framework.

Whether you're designing benchmark experiments or exploring real-world data — MissMecha lets you simulate and analyze missingness with clarity and control.

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⚡ Quick Preview

from missmecha import MissMechaGenerator

generator = MissMechaGenerator(
    info={
        0: {"mechanism": "mar", "type": 1, "rate": 0.3}
    }
)
generator.fit(X)
X_missing = generator.transform(X)

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Documentation & Notebooks

• Full documentation: [Link coming soon]
• Demo notebooks:
  • demo_generate.ipynb
  • demo_analysis.ipynb
  • demo_visual.ipynb

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Installation

pip install missmecha-py  # Coming soon on PyPI

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Author

Developed by Youran Zhou, PhD Candidate @ Deakin University
With support from the open-source research community ❤️

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📄 License

MIT License