pomp provides a very general realization of nonlinear partially-observed Markov processes (also known as nonlinear stochastic dynamical systems, hidden Markov models, and state space models). These are a generalization of linear state-space and hidden Markov models to nonlinear, non-Gaussian processes in either discrete or continuous time. Using pomp, one can implement a model by specifying its latent-state process and measurement components; the package uses these functions in algorithms to simulate, analyze, and fit the model to data.

Currently, pomp provides support for

pomp is also a platform upon which general inference algorithms for partially observed Markov processes can be implemented. We welcome contributions in the form of codes, examples, improvements to the documentation, bug reports, feature requests, and requests for help!

This website contains:

Although pomp is a mature package, it is actively maintained and new features are under development. If you come up with improvements, find bugs, or have suggestions or feature requests, please tell us about them using the package issues page.

To keep abreast of pomp news, view the pomp news blog and/or subscribe to the pomp RSS feed.


This software has been made possible by support from the U.S. National Science Foundation (Grants #EF-0545276, #EF-0430120), by the “Inference for Mechanistic Models” Working Group supported by the National Center for Ecological Analysis and Synthesis (a Center funded by N.S.F. (Grant #DEB-0553768), the University of California, Santa Barbara, and the State of California), and by the RAPIDD program of the Science & Technology Directorate, Department of Homeland Security and the Fogarty International Center, U.S. National Institutes of Health.