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pomp (version 1.10)

Statistical Inference for Partially Observed Markov Processes

Description

Tools for working with partially observed Markov process (POMP) models (also known as stochastic dynamical systems, hidden Markov models, and nonlinear, non-Gaussian, state-space models). The package 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 versatile platform for implementation of inference methods for general POMP models.

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Version

Install

install.packages('pomp')

Monthly Downloads

2,629

Version

1.10

License

GPL (>= 2)

Maintainer

Last Published

November 23rd, 2016

Functions in pomp (1.10)

Trajectory matching

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

Gompertz model with log-normal observations.
Ensemble Kalman filters

Ensemble Kalman filters
ricker

Ricker model with Poisson observations.
rw2

Two-dimensional random-walk process
Utilities for reproducibility

Tools for reproducible computations.
Approximate Bayesian computation

Estimation by approximate Bayesian computation (ABC)
Bayesian sequential Monte Carlo

The Liu and West Bayesian particle filter
B-splines

B-spline bases
Iterated filtering 2

IF2: Maximum likelihood by iterated, perturbed Bayes maps
Particle Markov Chain Monte Carlo

The particle Markov chain Metropolis-Hastings algorithm
Nonlinear forecasting

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

Definition and methods of the "pomp.fun" class
Probe functions

Some useful probes for partially-observed Markov processes
dacca

Model of cholera transmission for historic Bengal.
design

Design matrices for pomp calculations
Low-level-interface

pomp low-level interface
logmeanexp

The log-mean-exp trick
Simulated annealing

Simulated annealing with box constraints.
POMP simulation

Simulations of a partially-observed Markov process
blowflies

Model for Nicholson's blowflies.
Probes and synthetic likelihood

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

MCMC proposal distributions
sir

Compartmental epidemiological models
Power spectrum computation and matching

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

The Euler-multinomial distributions and Gamma white-noise processes
Example pomp models

Examples of the construction of POMP models
Iterated filtering

Maximum likelihood by iterated filtering
Childhood disease incidence data

Historical childhood disease incidence data
pomp methods

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

Constructor of the basic pomp object
parmat

Create a matrix of parameters
Particle filter

Particle filter