Multivariate spatio-temporal models are widely applicable, but specifying their structure is complicated and may inhibit wider use. We introduce the R package tinyVAST from two viewpoints: the software user and the statistician. From the user viewpoint, tinyVAST adapts a widely used formula interface to specify generalized additive models, and combines this with arguments to specify spatial and spatio-temporal interactions among variables. These interactions are specified using arrow notation (from structural equation models), or an extended arrow-and-lag notation that allows simultaneous, lagged, and recursive dependencies among variables over time. The user also specifies a spatial domain for areal (gridded), continuous (point-count), or stream-network data. From the statistician viewpoint, tinyVAST constructs sparse precision matrices representing multivariate spatio-temporal variation, and parameters are estimated by specifying a generalized linear mixed model (GLMM). This expressive interface encompasses vector autoregressive, empirical orthogonal functions, spatial factor analysis, and ARIMA models. To demonstrate, we fit to data from two survey platforms sampling corals, sponges, rockfishes, and flatfishes in the Gulf of Alaska and Aleutian Islands. We then compare eight alternative model structures using different assumptions about habitat drivers and survey detectability. Model selection suggests that towed-camera and bottom trawl gears have spatial variation in detectability but sample the same underlying density of flatfishes and rockfishes, and that rockfishes are positively associated with sponges while flatfishes are negatively associated with corals. We conclude that tinyVAST can be used to test complicated dependencies representing alternative structural assumptions for research and real-world policy evaluation.

翻译：多元时空模型应用广泛，但其结构指定的复杂性可能阻碍其更广泛的使用。我们从软件用户和统计学家两个视角介绍R包tinyVAST。从用户视角看，tinyVAST采用广泛使用的公式接口来指定广义可加模型，并结合参数来指定变量间的空间和时空交互作用。这些交互作用通过箭头符号（来自结构方程模型）或扩展的箭头-滞后符号来指定，允许变量间随时间变化的即时、滞后和递归依赖关系。用户还可以为面状（网格化）、连续（点计数）或河网数据指定空间域。从统计学家视角看，tinyVAST构建代表多元时空变异的稀疏精度矩阵，并通过指定广义线性混合模型（GLMM）来估计参数。这一表达性接口涵盖向量自回归、经验正交函数、空间因子分析和ARIMA模型。为进行演示，我们对来自阿拉斯加湾和阿留申群岛珊瑚、海绵、岩鱼和比目鱼两个采样平台的数据进行拟合，并基于栖息地驱动因素和调查可检测性的不同假设，比较了八种替代模型结构。模型选择表明，拖曳相机和底拖网设备在可检测性上存在空间变异，但采样的是相同的底层比目鱼和岩鱼密度；岩鱼与海绵呈正相关，而比目鱼与珊瑚呈负相关。我们得出结论：tinyVAST可用于检验代表研究及实际政策评估中不同结构假设的复杂依赖关系。