R help - Jul 2007 - New package: pomp, inference for partially-observed Markov processes

To: cran at r-project.org
Subject: New package: pomp, inference for partially-observed Markov processes

The new package 'pomp' is built around a very general realization of
nonlinear
partially-observed Markov processes (AKA state-space models, nonlinear 
stochastic dynamical systems). The user provides functions specifying the 
model's process and measurement components. The package's algorithms are
built on top of these functions. 

At the moment, algorithms are provided for particle filtering (AKA sequential 
importance sampling or sequential Monte Carlo) and the likelihood 
maximization by iterated filtering (MIF) method of Ionides, Breto, and King 
(PNAS, 103:18438-18443, 2006). 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.

Simple worked examples are provided in the form of a 
vignette, "random_walk_example".

The package is provided under the GPL. Contributions are welcome, as are 
comments, suggestions, examples, and bug reports.

The development of this package has been aided by support from the U.S. N.S.F 
(Grants #EF-0545276, #EF-0430120) and by the "Inference for Mechanistic 
Models" Working Group supported by the National Center for Ecological 
Analysis and Synthesis, a Center funded by NSF (Grant #DEB-0553768), the 
University of California, Santa Barbara, and the State of California.



-- 
Aaron A. King, Ph.D.
Ecology & Evolutionary Biology
University of Michigan
http://www.umich.edu/~kingaa
GPG Public Key: 0x2B00840F

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