missmecha-py 0.1.1

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

MissMecha

MissMecha is a Python package for the systematic simulation, visualization, and evaluation of missing data mechanisms.
It provides a unified and principled interface to generate, inspect, and analyze missingness — supporting research, benchmarking, and education.

Documentation: https://echoid.github.io/MissMecha/

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Highlights

• All About Missing Mechanisms

  • Simulate MCAR, MAR, and MNAR with flexible configuration
  • Currently includes:
    • 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 diagnose potential mechanism types (e.g., MCAR vs. MAR)

• Flexible Generator Interface

  • Column-wise or global simulation
  • fit / transform scikit-learn style API
  • Supports custom rates, dependencies, and label-based logic

• Evaluation Toolkit

  • Metrics including MSE, MAE, RMSE, and our custom AvgERR
  • Built-in support for Little’s MCAR test

• SimpleSmartImputer

  • Lightweight imputer that detects numerical vs. categorical columns
  • Applies mean/mode imputation with transparent diagnostics

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Motivation

Working with missing data often involves disjointed code and inconsistent assumptions.

MissMecha solves this by consolidating diverse missingness simulation strategies — across MCAR, MAR, and MNAR — into one consistent, structured Python framework.

Whether you're exploring datasets, designing simulations, or teaching statistics —
MissMecha helps you simulate and analyze missingness with clarity and control.

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⚡ Quick Preview

from missmecha import MissMechaGenerator
import numpy as np

X = np.random.rand(100, 5)

generator = MissMechaGenerator(
    mechanism="mar", mechanism_type=1, missing_rate=0.3
)
X_missing = generator.fit_transform(X)

Or configure per-column:

generator = MissMechaGenerator(
    info={
        0: {"mechanism": "mcar", "type": 1, "rate": 0.3},
        1: {"mechanism": "mnar", "type": 2, "rate": 0.4}
    }
)
X_missing = generator.fit_transform(X)

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Documentation & Demos

• Full documentation: https://echoid.github.io/MissMecha/

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Installation

pip install missmecha-py

Available on PyPI under the name missmecha-py.

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Author

Developed by Youran Zhou, PhD Candidate @ Deakin University

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License

MIT License

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