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statistical inference for
partially-observed Markov processes

pomp provides a very general realization of nonlinear partially-observed Markov processes (AKA nonlinear stochastic dynamical systems). These are a generalization of the familiar state-space and hidden Markov models to nonlinear, non-Gaussian processes in either discrete or continuous time. In pomp, one can implement a model by specifying its unobserved process and measurement components; the package uses these functions in algorithms to simulate, analyze, and fit the model to data.

At the moment, support is provided for

  • basic particle filtering (AKA sequential importance sampling or sequential Monte Carlo),
  • trajectory matching,
  • the approximate Bayesian sequential Monte Carlo algorithm of Liu & West (2001),
  • the particle Markov chain Monte Carlo method of Andrieu et al. (2010),
  • approximate Bayesian computation (ABC; Toni et al. 2009)
  • the iterated filtering method of Ionides, Breto, & King (2006),
  • probe-matching methods (e.g., Kendall et al. 1999)
  • the nonlinear forecasting method of Ellner & Kendall,and
  • power-spectrum-matching methods of Reuman et al. (2006).

Simple worked examples are provided in vignettes and in the examples directory of the installed package.

Future support for a variety of other algorithms is envisioned. A working group of the National Center for Ecological Analysis and Synthesis (NCEAS), "Inference for Mechanistic Models", is currently implementing additional methods for this package.

Please let the developers know if you find pomp useful, if you publish results obtained using it, if you come up with improvements, find bugs, or have suggestions or feature requests!

The package is provided under the GPL. Contributions are welcome, as are comments, suggestions, feature requests, examples, and bug reports. Please send these to kingaa at umich dot edu. pomp is under active development: new features are being added and old features are being improved. Although the developers will make efforts to preserve backward compatibility, we cannot absolutely guarantee backward compatibility. We will be sure to include warnings of changes that break backward compatibility in the NEWS file and the pomp-announce mailing list.

To keep abreast of new developments, subscribe to the pomp-announce mailing list.

link to NSF The development of this package has been made possible by support from the U.S. National Science Foundation (Grants #EF-0545276, #EF-0430120), by the "Inference for Mechanistic Models" Working Group supported by the National Center for Ecological Analysis and Synthesis, a Center funded by N.S.F. (Grant #DEB-0553768), the University of California, Santa Barbara, and the State of California, and by the RAPIDD program of the Science & Technology Directorate, Department of Homeland Security and the Fogarty International Center, U.S. National Institutes of Health.