  We consider inference for a class of nonlinear stochastic processes with
latent dynamic variables and spatial structure. The spatial structure takes the
form of a finite collection of spatial units that are dynamically coupled. We
assume that the latent processes have a Markovian structure and that
unit-specific noisy measurements are made. 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, and developing new inference approaches. We describe the spatPomp
implementations of some methods with scaling properties suited to SpatPOMP
models. 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.
