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pomp (version 0.30-1)

pomp-package: Partially-observed Markov processes

Description

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 might use pomp to fit a model to time-series data or simply to simulate a stochastic dynamical model. 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 and examples are given in the intro_to_pomp vignette.

Currently, pomp provides algorithms for (i) simulation of stochastic dynamical systems (see simulate), (ii) particle filtering (AKA sequential Monte Carlo or sequential importance sampling, see pfilter), (iii) the likelihood maximization by iterated filtering (MIF) method of Ionides, Breto, and King (PNAS, 103:18438-18443, 2006, see mif), and (iv) the nonlinear forecasting algorithm of Kendall, Ellner, et al. (Ecol. Monog. 75:259-276, 2005, see nlf). Future support for additional algorithms in envisioned, and implementations of the Bayesian sequential Monte Carlo approach of Liu & West and generalized probe-matching methods are currently under development. Much of the work in pomp has been done under the auspices of a working group of the National Center for Ecological Analysis and Synthesis (NCEAS), "Inference for Mechanistic Models".

The package is provided under the GNU Public License (GPL). Contributions are welcome, as are comments, suggestions for improvements, and bug reports.

Arguments

Classes

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.

Vignettes

The vignette intro_to_pomp illustrates the facilities of the package using familiar stochastic processes. Run vignette("intro_to_pomp") or look at the HTML documentation to view the vignette.

See Also

pomp, pfilter, simulate, mif, nlf