pomp: Statistical Inference for Partially Observed Markov Processes

Tools for working with partially observed Markov processes (POMPs, AKA stochastic dynamical systems, state-space models). 'pomp' 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 platform for the implementation of new inference methods.

Depends: R (≥ 3.1.0), methods
Imports: stats, graphics, digest, mvtnorm, deSolve, coda, subplex, nloptr
Suggests: magrittr, plyr, reshape2, ggplot2, knitr
Published: 2015-09-25
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]
Maintainer: Aaron A. King <kingaa at umich.edu>
Contact: kingaa at umich dot edu
BugReports: http://github.com/kingaa/pomp/issues
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL: http://kingaa.github.io/pomp
NeedsCompilation: yes
SystemRequirements: For Windows users, Rtools (see http://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_1.2.1.1.tar.gz
Windows binaries: r-devel: pomp_1.2.1.1.zip, r-release: pomp_1.2.1.1.zip, r-oldrel: pomp_1.2.1.1.zip
OS X Snow Leopard binaries: r-release: pomp_1.2.1.1.tgz, r-oldrel: pomp_0.53-5.tgz
OS X Mavericks binaries: r-release: pomp_1.2.1.1.tgz
Old sources: pomp archive

Reverse dependencies:

Reverse suggests: CollocInfer