一站式工具，模拟和分析数据缺失机制。
MissMecha: An All-in-One Python Package for Studying Missing Data Mechanisms
- 支持数值与类别变量的缺失机制仿真
- 覆盖MCAR、MAR、MNAR三类缺失场景
- 适合数据质量研究与教学使用
不完整数据是真实世界数据集中的常见挑战，常由复杂且不可观测的缺失机制驱动。模拟缺失已成为理解其对学习与分析影响的标准方法。然而，现有工具分散、机制有限，通常仅关注数值变量，忽视了真实表格数据的异质性。我们提出 MissMecha，一个开源 Python 工具包，用于在 MCAR、MAR 和 MNAR 假设下模拟、可视化和评估缺失数据。它同时支持数值与类别特征，可在混合类型表格数据中实现机制感知的研究。工具包含可视化诊断、MCAR 检测功能以及类型感知的插补评估指标。专为数据质量研究、基准测试和教学设计，提供统一平台，助力研究人员与从业者应对不完整数据问题。
原文摘要 · Abstract (English)
Incomplete data is a persistent challenge in real-world datasets, often governed by complex and unobservable missing mechanisms. Simulating missingness has become a standard approach for understanding its impact on learning and analysis. However, existing tools are fragmented, mechanism-limited, and typically focus only on numerical variables, overlooking the heterogeneous nature of real-world tabular data. We present MissMecha, an open-source Python toolkit for simulating, visualizing, and evaluating missing data under MCAR, MAR, and MNAR assumptions. MissMecha supports both numerical and categorical features, enabling mechanism-aware studies across mixed-type tabular datasets. It includes visual diagnostics, MCAR testing utilities, and type-aware imputation evaluation metrics. Designed to support data quality research, benchmarking, and education,MissMecha offers a unified platform for researchers and practitioners working with incomplete data.
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