scikit-covtest：Python中的协方差矩阵假设检验
scikit-covtest: Covariance Matrix Hypothesis Testing in Python
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中文总结 AI 辅助
针对Python缺乏全面协方差矩阵假设检验实现的问题，研发了开源Python包scikit-covtest，提供四类协方差检验及相关支撑功能，降低了相关方法在科学应用中的应用门槛。
中文摘要 AI 辅助
协方差矩阵在金融、机器学习、神经科学和遗传学等多个领域发挥核心作用，用于降维、连通性推断和风险估计等任务。许多此类应用需要检验协方差矩阵是否符合特定结构。尽管有多个R包提供这类检验的部分功能，但作为机器学习领域重要语言的Python，缺乏全面、经过充分测试的实现。为填补这一空白，我们推出scikit-covtest，这一Python包实现了四类协方差矩阵的多种假设检验：单位矩阵检验、球性检验、比例检验及两样本相等检验。该包提供一致的SciPy风格API、详尽文档，以及多重检验校正、合成数据生成和诊断评估等支撑功能。scikit-covtest为开源项目，可通过PyPI获取，降低了在科学应用中应用现代协方差检验方法的门槛。
英文摘要
Covariance matrices play a central role across diverse domains such as finance, machine learning, neuroscience, and genetics, where they are used for tasks including dimensionality reduction, connectivity inference, and risk estimation. Many of these applications require testing whether a covariance matrix follows a specific structure. While several R packages provide partial coverage of such tests, Python, an important language in machine learning, lacks a comprehensive, well-tested implementation. To address this gap, we introduce scikit-covtest, a Python package implementing a variety of hypothesis tests for covariance matrices spanning four categories: identity, sphericity, proportionality, and two-sample equality. The package provides a consistent SciPy-style API, extensive documentation, and supporting functionality for multiple testing correction, synthetic data generation, and diagnostic evaluation. scikit-covtest is open source, available through PyPI, and lowers the barrier to applying modern covariance-testing methods in scientific applications.
