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pomp (version 1.6)

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

6,668

Version

1.6

License

GPL (>= 2)

Maintainer

Aaron King

Last Published

June 30th, 2016

Functions in pomp (1.6)

dacca

Model of cholera transmission for historic Bengal.
Approximate Bayesian computation

Estimation by approximate Bayesian computation (ABC)
eulermultinom

The Euler-multinomial distributions and Gamma white-noise processes
Bayesian sequential Monte Carlo

The Liu and West Bayesian particle filter
design

Design matrices for pomp calculations
Example pomp models

Examples of the construction of POMP models
Utilities for reproducibility

Tools for reproducible computations.
B-splines

B-spline bases
Probe functions

Some useful probes for partially-observed Markov processes
blowflies

Model for Nicholson's blowflies.
parmat

Create a matrix of parameters
Childhood disease incidence data

Historical childhood disease incidence data
Ensemble Kalman filters

Ensemble Kalman filters
Particle filter

Particle filter
gompertz

Gompertz model with log-normal observations.
logmeanexp

The log-mean-exp trick
Iterated filtering

Maximum likelihood by iterated filtering
Low-level-interface

pomp low-level interface
Nonlinear forecasting

Parameter estimation my maximum simulated quasi-likelihood (nonlinear forecasting)
Iterated filtering 2

IF2: Maximum likelihood by iterated, perturbed Bayes maps
Simulated annealing

Simulated annealing with box constraints.
POMP simulation

Simulations of a partially-observed Markov process
Particle Markov Chain Monte Carlo

The particle Markov chain Metropolis-Hastings algorithm
pomp-fun

Definition and methods of the "pomp.fun" class
MCMC proposal distributions

MCMC proposal distributions
Probes and synthetic likelihood

Probe a partially-observed Markov process by computing summary statistics and the synthetic likelihood.
rw2

Two-dimensional random-walk process
ricker

Ricker model with Poisson observations.
pomp methods

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

Constructor of the basic pomp object
sir

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