Tools for data analysis 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.
|Author||Aaron A. King [aut, cre] (<https://orcid.org/0000-0001-6159-3207>), Edward L. Ionides [aut] (<https://orcid.org/0000-0002-4190-0174>), Carles Bretó [aut] (<https://orcid.org/0000-0003-4695-4902>), Stephen P. Ellner [ctb] (<https://orcid.org/0000-0002-8351-9734>), Matthew J. Ferrari [ctb], Sebastian Funk [ctb] (<https://orcid.org/0000-0002-2842-3406>), Steven G. Johnson [ctb], Bruce E. Kendall [ctb] (<https://orcid.org/0000-0003-1782-8106>), Michael Lavine [ctb], Dao Nguyen [ctb] (<https://orcid.org/0000-0003-2215-613X>), Eamon B. O'Dea [ctb] (<https://orcid.org/0000-0003-4748-683X>), Daniel C. Reuman [ctb], Helen Wearing [ctb] (<https://orcid.org/0000-0002-9837-9797>), Simon N. Wood [ctb] (<https://orcid.org/0000-0002-2034-7453>)|
|Maintainer||Aaron A. King <email@example.com>|
|Package repository||View on CRAN|
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