pomp: 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.

Vignettes Man pages API and functions Files

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 O'Dea [ctb]
Maintainer	Aaron A. King <[email protected]>
License	GPL-3
Version	1.18
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: `install.packages("pomp")`