pomp (1.8)

0 users

Statistical Inference for Partially Observed Markov Processes.

http://kingaa.github.io/pomp
http://cran.r-project.org/web/packages/pomp

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.

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], Steven G. Johnson [ctb]

License: GPL (>= 2)

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

Released 30 days ago.


48 previous versions

Ratings

Overall:

  (0 votes)

Documentation:

  (0 votes)

Log in to vote.

Reviews

No one has written a review of pomp yet. Want to be the first? Write one now.


Related packages: sde, CollocInfer, mkin, GillespieSSA, PBSmodelling, adaptivetau, pracma, PBSddesolve, nlmeODE, acp, depmix, deseasonalize, costat, CommonTrend, changepoint, cents, carx, bsts, brainwaver, cts(20 best matches, based on common tags.)


Search for pomp on google, google scholar, r-help, r-devel.

Visit pomp on R Graphical Manual.