Tools for working with partially observed Markov processes (POMPs, AKA stochastic dynamical systems, state-space models). 'pomp' 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 platform for the implementation of new inference methods.
|Author||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]|
|Date of publication||2015-07-06 10:49:42|
|Maintainer||Aaron A. King <firstname.lastname@example.org>|
|Package repository||View on R-Forge|
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