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Tools for data analysis with partially observed Markov process (POMP) models (also known as stochastic dynamical systems, hidden Markov models, and nonlinear, nonGaussian, statespace 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.
Package details 


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], Sebastian Funk [ctb], Steven G. Johnson [ctb], Eamon B. O'Dea [ctb] 
Maintainer  Aaron A. King <kingaa@umich.edu> 
License  GPL3 
Version  3.3 
URL  https://kingaa.github.io/pomp/ 
Package repository  View on CRAN 
Installation 
Install the latest version of this package by entering the following in R:

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