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pomp (version 1.4.1.1)

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

1.4.1.1

License

GPL (>= 2)

Maintainer

Aaron King

Last Published

March 28th, 2016

Functions in pomp (1.4.1.1)

ricker

Ricker model with Poisson observations.
POMP simulation

Simulations of a partially-observed Markov process
sir

Compartmental epidemiological models
eulermultinom

The Euler-multinomial distributions and Gamma white-noise processes
pomp constructor

Constructor of the basic POMP object
particles-mif

Generate particles from the user-specified distribution.
ou2

Two-dimensional discrete-time Ornstein-Uhlenbeck process
Trajectory matching

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

Design matrices for pomp calculations
pomp-package

Inference for partially observed Markov processes
pomp-fun

Definition and methods of the "pomp.fun" class
rw2

Two-dimensional random-walk process
parmat

Create a matrix of parameters
B-splines

B-spline bases
Bayesian sequential Monte Carlo

The Liu and West Bayesian particle filter
Childhood disease incidence data

Historical childhood disease incidence data
Probe functions

Some useful probes for partially-observed Markov processes
Probes and synthetic likelihood

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

C code snippets for accelerating computations
MCMC proposal distributions

MCMC proposal distributions
gompertz

Gompertz model with log-normal observations.
blowflies

Model for Nicholson's blowflies.
dacca

Model of cholera transmission for historic Bengal.
Approximate Bayesian computation

Estimation by approximate Bayesian computation (ABC)
Process model plugins

Plug-ins for state-process models
Nonlinear forecasting

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

IF2: Maximum likelihood by iterated, perturbed Bayes maps
logmeanexp

The log-mean-exp trick
Power spectrum computation and matching

Power spectrum computation and spectrum-matching for partially-observed Markov processes
Particle Markov Chain Monte Carlo

The particle Markov chain Metropolis-Hastings algorithm
Particle filter

Particle filter
pomp methods

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

Maximum likelihood by iterated filtering
Low-level-interface

pomp low-level interface
Simulated annealing

Simulated annealing with box constraints.
Example pomp models

Examples of the construction of POMP models
Utilities for reproducibility

Tools for reproducible computations.