pomp package provides facilities for inference using partially-observed Markov processes (AKA state-space models or nonlinear stochastic dynamical systems).
The user provides functions specifying the model's process and measurement components.
The package's algorithms are built on top of these functions.
At the moment, algorithms are provided for particle filtering (AKA sequential Monte Carlo or sequential importance sampling) and the likelihood maximization by iterated filtering (MIF) method of Ionides, Breto, and King (PNAS, 103:18438-18443, 2006).
Future support for a variety of other algorithms is envisioned.
A working group of the National Center for Ecological Analysis and Synthesis (NCEAS), "Inference for Mechanistic Models", is currently implementing additional methods for this package.The package is provided under the GPL. Contributions are welcome, as are comments, suggestions, and bug reports.
pomp, is provided to encode a partially-observed Markov process together with a multivariate data set. The class mif derives from class pomp and encodes the results of fitting the model to the data by the MIF algorithm.
vignette('intro_to_pomp') or look at the HTML documentation to view the vignette.pomp-class, pomp, mif-class, mif