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.

Version: 1.16
Depends: R (≥ 3.1.2), methods
Imports: stats, graphics, digest, mvtnorm, deSolve, coda, subplex, nloptr
Suggests: magrittr, plyr, reshape2, ggplot2, knitr
Published: 2017-12-17
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


Reference manual: pomp.pdf
Package source: pomp_1.16.tar.gz
Windows binaries: r-devel: pomp_1.16.zip, r-release: pomp_1.16.zip, r-oldrel: pomp_1.16.zip
OS X El Capitan binaries: r-release: pomp_1.16.tgz
OS X Mavericks binaries: r-oldrel: pomp_1.16.tgz
Old sources: pomp archive

Reverse dependencies:

Reverse suggests: CollocInfer, spaero


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