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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, 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: | 5.11 |
Depends: | R (≥ 4.1.0) |
Imports: | methods, stats, graphics, digest, mvtnorm, deSolve, coda, data.table |
Suggests: | ggplot2, knitr, dplyr, tidyr, subplex, nloptr |
Published: | 2024-09-12 |
DOI: | 10.32614/CRAN.package.pomp |
Author: | Aaron A. King [aut, cre], Edward L. Ionides [aut], Carles Bretó [aut], Stephen P. Ellner [ctb], Matthew J. Ferrari [ctb], Sebastian Funk [ctb], Steven G. Johnson [ctb], Bruce E. Kendall [ctb], Michael Lavine [ctb], Dao Nguyen [ctb], Eamon B. O'Dea [ctb], Daniel C. Reuman [ctb], Helen Wearing [ctb], Simon N. Wood [ctb] |
Maintainer: | Aaron A. King <kingaa at umich.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: | README NEWS |
In views: | DifferentialEquations, Epidemiology, TimeSeries |
CRAN checks: | pomp results |
Reference manual: | pomp.pdf |
Package source: | pomp_5.11.tar.gz |
Windows binaries: | r-devel: pomp_5.11.zip, r-release: pomp_5.11.zip, r-oldrel: pomp_5.11.zip |
macOS binaries: | r-release (arm64): pomp_5.11.tgz, r-oldrel (arm64): pomp_5.11.tgz, r-release (x86_64): pomp_5.11.tgz, r-oldrel (x86_64): pomp_5.11.tgz |
Old sources: | pomp archive |
Reverse depends: | panelPomp, spatPomp |
Reverse imports: | DTAT, stops |
Reverse linking to: | spatPomp |
Reverse suggests: | CollocInfer, epimdr, epimdr2, spaero |
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.