pomp-package {pomp} | R Documentation

## 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`

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. a spectral probe-matching method (Reuman et al. 2006, 2008); see `spect.match`

8. synthetic likelihood a la Wood (2010); see `probe`

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.

### Author(s)

Aaron A. King

### References

A. A. King, D. Nguyen, and E. L. Ionides (2016) Statistical Inference for Partially Observed Markov Processes via the Package pomp. Journal of Statistical Software 69(12): 1–43. An updated version of this paper is available on the package website.

See the package website, https://kingaa.github.io/pomp/, for more references.

### See Also

Other information on model implementation: `Csnippet`, `accumulators`, `covariate_table`, `distributions`, `dmeasure_spec`, `dprocess_spec`, `parameter_trans`, `prior_spec`, `rinit_spec`, `rmeasure_spec`, `rprocess_spec`, `skeleton_spec`, `transformations`, `userdata`

Other pomp parameter estimation methods: `abc`, `bsmc2`, `kalman`, `mif2`, `nlf`, `pmcmc`, `probe.match`, `spect.match`

Other elementary POMP methods: `pfilter`, `probe`, `simulate`, `spect`

[Package pomp version 2.3 Index]