pomp (2.4)

Statistical Inference for Partially Observed Markov Processes.


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.

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, magrittr, mvtnorm, plyr, reshape2, ggplot2, subplex, knitr, dplyr, tidyr
Reverse suggests: CollocInfer, epimdr, spaero

Released about 1 month ago.