The pomp package provides facilities for inference on time series data using partially-observed Markov processes (AKA state-space models or nonlinear stochastic dynamical systems).
One can use pomp to fit nonlinear, non-Gaussian dynamic models to time-series data.
The first step in using pomp is to encode one's model and data in an object of class pomp.
One does this via a call to pomp, which involves specifying the process and measurement components of the model in one or more of a variety of ways.
Details on this are given in the documentation for the pomp function.
Examples are given in the http://pomp.r-forge.r-project.org/vignettes/intro_to_pomp.pdf{Introduction to pomp} document, in the demos (demo(package='pomp')), and via the pompExample function. pomp version [echo=F,stage=install,results=text]{packageDescription("pomp",fields="Version")} 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 method of Ionides et al. (2006, 2011); see
mif - 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
bsmc - basic trajectory matching; see
traj.match.
The package also provides various tools for plotting and extracting information on models and data as well as an API for algorithm development.
Future support for additional algorithms in envisioned.
Much of the groundwork for pomp was laid by a working group of the National Center for Ecological Analysis and Synthesis (NCEAS), Inference for Mechanistic Models.
pomp is provided under the GNU Public License (GPL).
Contributions are welcome, as are comments, feature requests, and bug reports.
See the package website http://pomp.r-forge.r-project.org for more information, access to the package mailing list, links to the authors' websites, references to the literature, and up-to-date versions of the package source and documentation.
{
pomp makes extensive use of S4 classes.
The basic class, pomp, encodes a partially-observed Markov process together with a uni- or multi-variate data set and (possibly) parameters.
}
{
The http://pomp.r-forge.r-project.org/vignettes/intro_to_pomp.pdf{Introduction to pomp} document illustrates the facilities of the package using familiar stochastic processes.
The http://pomp.r-forge.r-project.org/vignettes/advanced_topics_in_pomp.pdf{Advanced topics in pomp} document discusses the low-level interface and some more advanced modeling techniques.
These documents can be viewed at http://pomp.r-forge.r-project.org.
}
See the package website, http://pomp.r-forge.r-project.org, for the references.
[object Object]
pomp,
pfilter,
simulate,
trajectory,
mif,
nlf,
probe.match,
traj.match,
bsmc,
pmcmc
models
ts