pomp-package: Inference for partially observed Markov processes

In pomp: Statistical Inference for Partially Observed Markov Processes

Inference for partially observed Markov processes

Description

The pomp package provides facilities for inference on time series data using partially-observed Markov process (POMP) models. These models are also known as state-space models, hidden Markov models, or nonlinear stochastic dynamical systems. One can use pomp to fit nonlinear, non-Gaussian dynamic models to time-series data. The package is both a set of tools for data analysis and a platform upon which statistical inference methods for POMP models can be implemented.

Data analysis using pomp

pomp provides algorithms for:

1. Simulation of stochastic dynamical systems; see simulate.

2. Particle filtering (AKA sequential Monte Carlo or sequential importance sampling); see pfilter and wpfilter.

3. The iterated filtering methods of Ionides et al. (2006, 2011, 2015); see mif2.

4. The nonlinear forecasting algorithm of Kendall et al. (2005); see nlf.

5. The particle MCMC approach of Andrieu et al. (2010); see pmcmc.

6. The probe-matching method of Kendall et al. (1999, 2005); see probe_match.

7. Synthetic likelihood a la Wood (2010); see probe.

8. A spectral probe-matching method (Reuman et al. 2006, 2008); see spect_match.

9. Approximate Bayesian computation (Toni et al. 2009); see abc.

10. The approximate Bayesian sequential Monte Carlo scheme of Liu & West (2001); see bsmc2.

11. Ensemble and ensemble adjusted Kalman filters; see kalman.

12. Simple trajectory matching; see traj_match.

The package also provides various tools for plotting and extracting information on models and data.

Structure of the package

pomp algorithms are arranged into several levels. At the top level, estimation algorithms estimate model parameters and return information needed for other aspects of inference. Elementary algorithms perform common operations on POMP models, including simulation, filtering, and application of diagnostic probes; these functions may be useful in inference, but they do not themselves perform estimation. At the lowest level, workhorse functions provide the interface to basic POMP model components. Beyond these, pomp provides a variety of auxiliary functions for manipulating and extracting information from ‘pomp’ objects, producing diagnostic plots, facilitating reproducible computations, and so on.

Implementing a model

The basic structure at the heart of the package is the ‘pomp object’. This is a container holding a time series of data (possibly multivariate) and a model. The model is specified by specifying some or all of its basic model components. One does this using the basic component arguments to the pomp constructor. One can also add, modify, or delete basic model components “on the fly” in any pomp function that accepts them.

Documentation and examples

The package contains a number of examples. Some of these are included in the help pages. In addition, several pre-built POMP models are included with the package. Tutorials and other documentation, including a package FAQ, are available from the package website.

Useful links

• pomp homepage: https://kingaa.github.io/pomp/

• Report bugs to: https://github.com/kingaa/pomp/issues

• Frequently asked questions: https://kingaa.github.io/pomp/FAQ.html

• User guides and tutorials: https://kingaa.github.io/pomp/docs.html

• pomp news: https://kingaa.github.io/pomp/blog.html

Citing pomp

Execute citation("pomp") to view the correct citation for publications.

Author(s)

Aaron A. King

References

\King

2016

See the package website for more references, including many publications that use pomp.

See Also

More on implementing POMP models: Csnippet, accumvars, basic_components, betabinomial, covariates, dinit_spec, dmeasure_spec, dprocess_spec, emeasure_spec, eulermultinom, parameter_trans(), pomp_constructor, prior_spec, rinit_spec, rmeasure_spec, rprocess_spec, skeleton_spec, transformations, userdata, vmeasure_spec

More on pomp workhorse functions: dinit(), dmeasure(), dprior(), dprocess(), emeasure(), flow(), partrans(), rinit(), rmeasure(), rprior(), rprocess(), skeleton(), vmeasure(), workhorses

More on pomp estimation algorithms: abc(), bsmc2(), estimation_algorithms, mif2(), nlf, pmcmc(), probe_match, spect_match

More on pomp elementary algorithms: elementary_algorithms, kalman, pfilter(), probe(), simulate(), spect(), trajectory(), wpfilter()

pomp documentation built on Aug. 8, 2023, 1:08 a.m.