AI 中文总结
该研究介绍R语言的BCSreg包，用于拟合非负数据的灵活回归模型并提供专属诊断工具，解决标准模型难以处理非负复杂数据的问题，通过实际数据应用验证其功能。
AI 中文摘要
应用统计学中常出现具有高偏度和厚尾特征的连续正数据，部分应用场景中这类特征还伴随零点处的点质量，形成混合离散-连续分布的非负响应。标准回归模型往往无法充分捕捉这些复杂特征，需要更灵活的方法。本文介绍R语言的BCSreg包，该包提供统一且全面的计算框架，用于拟合正连续数据的Box-Cox对称回归模型、对数对称回归模型，以及混合非负数据的零调整扩展模型。这类广泛的模型类别可适应不同程度的偏度和尾部厚度，同时允许参数在数据原始尺度上直接解释。通过友好的多部分公式接口，BCSreg包让从业者可同时指定尺度参数（与响应分位数成比例）、相对离散度的回归结构，必要时还可指定零值出现概率的回归结构。此外，该包提供专为这些模型类别定制的完整诊断工具集，包括分位数残差、模拟包络及影响诊断。本文通过实际数据应用展示了该包的功能与能力。
英文摘要
Continuous positive data characterized by high skewness and heavy tails frequently arise in applied statistics. In other applications, these characteristics are accompanied by a point mass at zero, resulting in a non-negative response with a mixed discrete-continuous distribution. Standard regression models often fail to capture these complex features adequately, requiring more flexible approaches. In this paper, we introduce the BCSreg package for R, which provides a comprehensive and unified computational framework for fitting Box-Cox symmetric and log-symmetric regression models for positive continuous data and their zero-adjusted extensions for mixed non-negative data. These broad classes of models accommodate varying degrees of skewness and tail-heaviness while allowing the parameters to be interpreted directly on the original scale of the data. Through a user-friendly multi-part formula interface, the BCSreg package allows practitioners to simultaneously specify regression structures for the scale parameter (which is proportional to the quantiles of the response), the relative dispersion, and, when appropriate, the probability of zero occurrences. Furthermore, the package provides a complete suite of diagnostic tools specifically tailored to these classes of models, including randomized quantile residuals, simulated envelopes, and influence diagnostics. The package's features and capabilities are illustrated through applications to real data.
