pomp: Statistical Inference for Partially Observed Markov Processes (original) (raw)

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: 6.1
Depends: R (≥ 4.1.0)
Imports: methods, stats, graphics, digest, mvtnorm, deSolve, coda, data.table
Suggests: ggplot2, knitr, dplyr, tidyr, subplex, nloptr
Published: 2025-01-08
DOI: 10.32614/CRAN.package.pomp
Author: Aaron A. King ORCID iD [aut, cre], Edward L. Ionides ORCID iD [aut], Carles Bretó ORCID iD [aut], Stephen P. Ellner ORCID iD [ctb], Matthew J. FerrariORCID iD [ctb], Sebastian Funk ORCID iD [ctb], Steven G. Johnson [ctb], Bruce E. Kendall ORCID iD [ctb], Michael Lavine [ctb], Dao Nguyen ORCID iD [ctb], Eamon B. O'Dea ORCID iD [ctb], Daniel C. Reuman [ctb], Helen Wearing ORCID iD [ctb], Simon N. Wood ORCID iD [ctb]
Maintainer: Aaron A. King
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

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