pomp (1.5)

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.

Maintainer: Aaron A. King
Author(s): 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]

License: GPL (>= 2)

Uses: coda, deSolve, digest, mvtnorm, nloptr, subplex, ggplot2, plyr, reshape2, knitr, magrittr
Reverse suggests: CollocInfer, epimdr, spaero

Released about 3 years ago.