pomp (1.19)

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Statistical Inference for Partially Observed Markov Processes.


Tools for working with partially observed Markov process (POMP) models (also known as stochastic dynamical systems, hidden Markov models, and nonlinear, non-Gaussian, state-space models). The package provides facilities for implementing POMP models, simulating them, and fitting them to time series data by a variety of frequentist and Bayesian methods. It is also a versatile platform for implementation of inference methods for general POMP models.

Maintainer: Aaron A. King
Author(s): Aaron A. King [aut, cre], Edward L. Ionides [aut], Carles Breto [aut], Stephen P. Ellner [ctb], Matthew J. Ferrari [ctb], Bruce E. Kendall [ctb], Michael Lavine [ctb], Dao Nguyen [ctb], Daniel C. Reuman [ctb], Helen Wearing [ctb], Simon N. Wood [ctb], Sebastian Funk [ctb], Steven G. Johnson [ctb], Eamon O'Dea [ctb]

License: GPL-3

Uses: coda, deSolve, digest, mvtnorm, nloptr, subplex, ggplot2, plyr, reshape2, knitr, magrittr
Reverse suggests: CollocInfer, epimdr, spaero

Released 2 months ago.

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