We describe a computational framework for modeling and statistical inference on high-dimensional stochastic dynamic systems. Our primary motivation is the investigation of metapopulation dynamics arising from a collection of spatially distributed, interacting biological populations. To make progress on this goal, we embed it in a more general problem: inference for a collection of interacting partially observed nonlinear non-Gaussian stochastic processes. Each process in the collection is called a unit; in the case of spatiotemporal models, the units correspond to distinct spatial locations. The dynamic state for each unit may be discrete or continuous, scalar or vector valued. In metapopulation applications, the state can represent a structured population or the abundances of a collection of species at a single location. We consider models where the collection of states has a Markov property. A sequence of noisy measurements is made on each unit, resulting in a collection of time series. A model of this form is called a spatiotemporal partially observed Markov process (SpatPOMP). The R package spatPomp provides an environment for implementing SpatPOMP models, analyzing data using existing methods, and developing new inference approaches. Our presentation of spatPomp reviews various methodologies in a unifying notational framework. We demonstrate the package on a simple Gaussian system and on a nontrivial epidemiological model for measles transmission within and between cities. We show how to construct user-specified SpatPOMP models within spatPomp.

翻译：本文描述了一个面向高维随机动态系统的建模与统计推断计算框架。我们的主要动机是研究由空间分布且相互作用的生物种群所构成的集合种群动力学。为实现这一目标，我们将其嵌入到一个更一般的问题中：对一组相互作用的、部分观测的非线性非高斯随机过程进行推断。该集合中的每个过程称为一个单元；在时空模型中，单元对应于不同的空间位置。每个单元的动态状态可以是离散或连续的、标量或向量值。在集合种群应用中，状态可以表示结构化的种群或单一地点内多个物种的丰度。我们考虑状态集合具有马尔可夫性的模型。对每个单元进行一系列含噪测量，从而得到一组时间序列。这种形式的模型称为时空部分观测马尔可夫过程（SpatPOMP）。R包spatPomp为实施SpatPOMP模型、利用现有方法分析数据以及开发新的推断方法提供了环境。我们在统一的符号框架下综述了spatPomp所涉及的多种方法论，并在简单高斯系统以及描述城市内和城市间麻疹传播的流行病学非平凡模型上演示了该包的应用，同时展示了如何在spatPomp中构造用户自定义的SpatPOMP模型。