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.
| Version: |
3.1 |
| Depends: |
R (≥ 4.0.0), methods |
| Imports: |
stats, graphics, digest, mvtnorm, deSolve, coda, reshape2, magrittr, plyr |
| Suggests: |
ggplot2, knitr, tidyr, dplyr, subplex, nloptr |
| Published: |
2020-07-05 |
| 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 <kingaa at umich.edu> |
| Contact: |
kingaa at umich dot edu |
| BugReports: |
https://github.com/kingaa/pomp/issues/ |
| License: |
GPL-3 |
| URL: |
https://kingaa.github.io/pomp/ |
| NeedsCompilation: |
yes |
| SystemRequirements: |
For Windows users, Rtools (see
https://cran.r-project.org/bin/windows/Rtools/). |
| Citation: |
pomp citation info |
| Materials: |
NEWS |
| In views: |
DifferentialEquations, TimeSeries |
| CRAN checks: |
pomp results |