  Partially observed Markov process (POMP) models are powerful tools for time
series modeling and analysis. Inherited the flexible framework of R package
pomp, the is2 package extends some useful Monte Carlo statistical methodologies
to improve on convergence rates. A variety of efficient statistical methods for
POMP models have been developed including fixed lag smoothing, second-order
iterated smoothing, momentum iterated filtering, average iterated filtering,
accelerate iterated filtering and particle iterated filtering. In this paper,
we show the utility of these methodologies based on two toy problems. We also
demonstrate the potential of some methods in a more complex model, employing a
nonlinear epidemiological model with a discrete population, seasonality, and
extra-demographic stochasticity. We discuss the extension beyond POMP models
and the development of additional methods within the framework provided by is2.
