pomp: 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.

Version: 3.2
Depends: R (≥ 4.0.0), methods
Imports: stats, graphics, digest, mvtnorm, deSolve, coda, reshape2, magrittr, plyr
Suggests: ggplot2, knitr, tidyr, dplyr, subplex, nloptr
Published: 2020-12-03
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 B. 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


Reference manual: pomp.pdf
Package source: pomp_3.2.tar.gz
Windows binaries: r-devel: pomp_3.2.zip, r-release: pomp_3.2.zip, r-oldrel: pomp_2.8.zip
macOS binaries: r-release: pomp_3.2.tgz, r-oldrel: pomp_2.8.tgz
Old sources: pomp archive

Reverse dependencies:

Reverse imports: DTAT
Reverse suggests: CollocInfer, epimdr, spaero


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