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pomp (version 1.7)

Statistical Inference for Partially Observed Markov Processes

Description

Tools for working with partially observed Markov processes (POMPs, AKA stochastic dynamical systems, state-space models). 'pomp' 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 platform for the implementation of new inference methods.

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Version

Install

install.packages('pomp')

Monthly Downloads

2,074

Version

1.7

License

GPL (>= 2)

Maintainer

Aaron King

Last Published

July 31st, 2016

Functions in pomp (1.7)

Utilities for reproducibility

Tools for reproducible computations.

Create a matrix of parameters

The log-mean-exp trick

Childhood disease incidence data

Historical childhood disease incidence data

Particle filter

Particle filter

Ensemble Kalman filters

Ensemble Kalman filters

Iterated filtering

Maximum likelihood by iterated filtering

Iterated filtering 2

IF2: Maximum likelihood by iterated, perturbed Bayes maps

Low-level-interface

pomp low-level interface

Gompertz model with log-normal observations.

Nonlinear forecasting

Parameter estimation my maximum simulated quasi-likelihood (nonlinear forecasting)

Probes and synthetic likelihood

Probe a partially-observed Markov process by computing summary statistics and the synthetic likelihood.

Ricker model with Poisson observations.

POMP simulation

Simulations of a partially-observed Markov process

Definition and methods of the "pomp.fun" class

Particle Markov Chain Monte Carlo

The particle Markov chain Metropolis-Hastings algorithm

Two-dimensional random-walk process

Simulated annealing

Simulated annealing with box constraints.

Functions for manipulating, displaying, and extracting information from objects of the pomp class

MCMC proposal distributions

MCMC proposal distributions

pomp constructor

Constructor of the basic pomp object

Compartmental epidemiological models

Power spectrum computation and matching

Power spectrum computation and spectrum-matching for partially-observed Markov processes

Trajectory matching

Parameter estimation by fitting the trajectory of a model's deterministic skeleton to data

Bayesian sequential Monte Carlo

The Liu and West Bayesian particle filter

Design matrices for pomp calculations

Probe functions

Some useful probes for partially-observed Markov processes

Approximate Bayesian computation

Estimation by approximate Bayesian computation (ABC)

Model for Nicholson's blowflies.

Model of cholera transmission for historic Bengal.

The Euler-multinomial distributions and Gamma white-noise processes

Example pomp models

Examples of the construction of POMP models