Crossover designs are widely applied in medicine, agriculture, and other biological sciences, yet their analysis remains challenging due to longitudinal observations within each unit and the presence of carry-over effects. Despite their prevalence, there is no comprehensive R package dedicated to the statistical modeling of crossover data. The CrossCarry package addresses this gap by providing a flexible and open-source framework for analyzing any crossover design with response variables from the exponential family, with or without washout periods. It extends the generalized estimating equations (GEE) methodology by incorporating correlation structures specifically tailored to crossover data, capturing both within- and between-period dependencies. Moreover, CrossCarry integrates a parametric component for treatment effects and a nonparametric spline-based component for time and carry-over effects. This combination allows users to model complex correlation patterns and temporal structures with minimal coding effort. By offering a domain-independent implementation of advanced statistical methodology, CrossCarry facilitates reproducible research and promotes the reuse of robust analytical tools across disciplines. Its potential applications span medical trials, agricultural field experiments, and other areas where crossover designs are essential, thus contributing to broader scientific discovery and cross-domain methodological standardization.

翻译：交叉设计在医学、农业及其他生物科学领域应用广泛，但由于每个单元内的纵向观测及遗留效应的存在，其数据分析仍具挑战性。尽管交叉设计普遍使用，目前尚无专门用于交叉数据统计建模的综合性R软件包。CrossCarry软件包填补了这一空白，为分析来自指数族响应变量的任意交叉设计（无论是否存在洗脱期）提供了一个灵活的开源框架。该软件包扩展了广义估计方程（GEE）方法，整合了专门针对交叉数据定制的相关结构，能够同时捕捉周期内和周期间的依赖关系。此外，CrossCarry集成了处理效应的参数化组件以及基于非参数样条的时间与遗留效应组件。这种组合使用户能够以最少的编码工作量建模复杂的相关模式和时间结构。通过提供领域无关的高级统计方法实现，CrossCarry促进了可重复性研究，并推动了跨学科稳健分析工具的重用。其潜在应用涵盖医学试验、农业田间实验及其他依赖交叉设计的重要领域，从而为更广泛的科学发现及跨领域方法学标准化做出贡献。