The pomp package provides facilities for inference on time series data using partially-observed Markov process (POMP) models. These models are also known as state-space models, hidden Markov models, or nonlinear stochastic dynamical systems. One can use pomp to fit nonlinear, non-Gaussian dynamic models to time-series data. The package is both a set of tools for data analysis and a platform upon which statistical inference methods for POMP models can be implemented.
pomp provides algorithms for
simulation of stochastic
dynamical systems; see simulate
particle filtering (AKA sequential Monte Carlo or sequential importance
sampling); see pfilter
the iterated filtering methods
of Ionides et al. (2006, 2011, 2015); see mif2
the
nonlinear forecasting algorithm of Kendall et al. (2005); see
nlf
the particle MCMC approach of Andrieu et al. (2010); see pmcmc
the probe-matching method of Kendall et al. (1999, 2005); see probe.match
a spectral probe-matching method (Reuman et al. 2006, 2008); see
spect.match
synthetic likelihood a la Wood (2010); see probe
approximate Bayesian computation (Toni et al. 2009); see abc
the approximate Bayesian sequential
Monte Carlo scheme of Liu & West (2001); see bsmc2
ensemble and ensemble adjusted Kalman filters; see kalman
simple trajectory matching; see traj.match.
The package also provides various tools for plotting and extracting information on models and data.
A. A. King, D. Nguyen, and E. L. Ionides (2016) Statistical Inference for Partially Observed Markov Processes via the Package pomp. Journal of Statistical Software 69(12): 1--43. An updated version of this paper is available on the package website.
See the package website, https://kingaa.github.io/pomp/, for more references.
Other information on model implementation: Csnippet,
accumulators,
covariate_table,
distributions, dmeasure_spec,
dprocess_spec,
parameter_trans, prior_spec,
rinit_spec, rmeasure_spec,
rprocess_spec, skeleton_spec,
transformations, userdata
Other pomp parameter estimation methods: abc,
bsmc2, kalman,
mif2, nlf,
pmcmc, probe.match,
spect.match
Other elementary POMP methods: pfilter,
probe, simulate,
spect