pomp: Statistical Inference for Partially Observed Markov Processes
Version 1.13

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.

Getting started

Package details

AuthorAaron 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]
Date of publication2017-07-18 11:46:46 UTC
MaintainerAaron A. King <kingaa@umich.edu>
LicenseGPL (>= 2)
URL http://kingaa.github.io/pomp
Package repositoryView on CRAN
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pomp documentation built on July 18, 2017, 5:01 p.m.