[R-pkgs] New package: pomp, inference for partially-observed Markov processes
aaron.king at umich.edu
Tue Jul 24 14:05:19 CEST 2007
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
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
GPG Public Key: 0x2B00840F
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