pomp: Statistical Inference for Partially Observed Markov Processes

Tools for data analysis 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.

Package details

AuthorAaron A. King [aut, cre], Edward L. Ionides [aut], Carles Breto [aut], Stephen P. Ellner [ctb], Matthew J. Ferrari [ctb], Sebastian Funk [ctb], Steven G. Johnson [ctb], Bruce E. Kendall [ctb], Michael Lavine [ctb], Dao Nguyen [ctb], Eamon B. O'Dea [ctb], Daniel C. Reuman [ctb], Helen Wearing [ctb], Simon N. Wood [ctb]
MaintainerAaron A. King <kingaa@umich.edu>
URL https://kingaa.github.io/pomp/
Package repositoryView on CRAN
Installation Install the latest version of this package by entering the following in R:

Try the pomp package in your browser

Any scripts or data that you put into this service are public.

pomp documentation built on April 12, 2022, 1:06 a.m.