Structural Equation Modeling (SEM) is a flexible statistical technique with multiple applications, including behavioral genetics and social sciences. Building on the original design of the umx package, which improved accessibility to OpenMx by specifying a concise syntax, umx v4.5 extends functionality for longitudinal and causal twin designs while improving interoperability with graphical modelling tools such as Onyx. New capabilities include: classic and modern cross-lagged panel models; Mendelian Randomization Direction-of-Causation (MR-DoC) twin models incorporating polygenic scores as instruments; support for definition variables directly in umxRAM(); a workflow for importing paths from Ωnyx; a dedicated function for incorporating censored variables' data into models, particularly valuable in biomarker research; improved covariate placeholder handling for definition variables; sex-limitation modelling across five twin groups, accommodating quantitative and qualitative sex differences; and covariate residualization in wide- or long-format data. These new functionalities accelerate reproducible, reliable, publication-ready twin and family modelling, and integrated journal-quality reporting, thereby lowering barriers to genetic epidemiological analyzes.

翻译：结构方程建模（SEM）是一种灵活的统计技术，在行为遗传学和社会科学等多个领域均有应用。umx 软件包最初的设计通过提供简洁的语法，提升了 OpenMx 的可访问性。在此基础上，umx v4.5 扩展了纵向与因果双生子设计的功能，同时增强了与 Onyx 等图形建模工具的互操作性。新增功能包括：经典与现代交叉滞后面板模型；整合多基因评分作为工具的孟德尔随机化因果方向（MR-DoC）双生子模型；在 umxRAM() 中直接支持定义变量；从 Ωnyx 导入路径的工作流程；用于将删失变量数据纳入模型的专用函数（在生物标志物研究中尤其有价值）；改进的定义变量协变量占位符处理；涵盖五个双生子组的性别限制模型，可适应定量与定性的性别差异；以及对宽格式或长格式数据的协变量残差化处理。这些新功能加速了可重复、可靠且达到发表水平的双生子与家庭建模，并集成了期刊质量的报告生成，从而降低了遗传流行病学分析的门槛。