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pomp (version 1.3.1.1)

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

1.3.1.1

License

GPL (>= 2)

Maintainer

Aaron King

Last Published

February 29th, 2016

Functions in pomp (1.3.1.1)

pomp-fun

Definition and methods of the "pomp.fun" class
Approximate Bayesian computation

Estimation by approximate Bayesian computation (ABC)
dacca

Model of cholera transmission for historic Bengal.
sir

Compartmental epidemiological models
Probe functions

Some useful probes for partially-observed Markov processes
blowflies

Model for Nicholson's blowflies.
particles-mif

Generate particles from the user-specified distribution.
parmat

Create a matrix of parameters
Process model plugins

Plug-ins for state-process models
POMP simulation

Simulations of a partially-observed Markov process
eulermultinom

The Euler-multinomial distributions and Gamma white-noise processes
Particle Markov Chain Monte Carlo

The particle Markov chain Metropolis-Hastings algorithm
Childhood disease incidence data

Historical childhood disease incidence data
design

Design matrices for pomp calculations
gompertz

Gompertz model with log-normal observations.
Iterated filtering

Maximum likelihood by iterated filtering
B-splines

B-spline bases
Bayesian sequential Monte Carlo

The Liu and West Bayesian particle filter
Probes and synthetic likelihood

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

Functions for manipulating, displaying, and extracting information from objects of the pomp class
Low-level-interface

pomp low-level interface
pomp-package

Inference for partially observed Markov processes
Power spectrum computation and matching

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

Ricker model with Poisson observations.
Utilities for reproducibility

Tools for reproducible computations.
Iterated filtering 2

IF2: Maximum likelihood by iterated, perturbed Bayes maps
Nonlinear forecasting

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

Simulated annealing with box constraints.
pomp constructor

Constructor of the basic POMP object
Example pomp models

Examples of the construction of POMP models
Particle filter

Particle filter
pompBuilder

Write, compile, and build a pomp object using native codes
MCMC proposal distributions

MCMC proposal distributions
logmeanexp

The log-mean-exp trick
Csnippet

C code snippets for accelerating computations
ou2

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

Parameter estimation by fitting the trajectory of a model's deterministic skeleton to data
rw2

Two-dimensional random-walk process