  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.
