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pomp (version 0.49-1)

Statistical inference for partially observed Markov processes

Description

Inference methods for partially-observed Markov processes

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Version

Install

install.packages('pomp')

Monthly Downloads

1,709

Version

0.49-1

License

GPL (>= 2)

Maintainer

Aaron King

Last Published

March 23rd, 2014

Functions in pomp (0.49-1)

eulermultinom

Euler-multinomial death process
pfilter-methods

Methods of the "pfilterd.pomp" class
init.state-pomp

Return a matrix of initial conditions given a vector of parameters and an initial time.
verhulst

Simple Verhulst-Pearl (logistic) model.
pomp-class

Partially-observed Markov process class
mif-class

The "mif" class
parmat

Create a matrix of parameters
rprocess-pomp

Simulate the process model of a partially-observed Markov process
LondonYorke

Historical childhood disease incidence data
mif

The MIF algorithm
basic.probes

Some probes for partially-observed Markov processes
pompExample

Pre-built examples of pomp objects.
skeleton-pomp

Evaluate the deterministic skeleton at the given points in state space.
sobol

Sobol' low-discrepancy sequence
traj.match

Trajectory matching
pmcmc

The PMCMC algorithm
abc

The ABC algorithm
gompertz

Gompertz model with log-normal observations.
pomp-fun

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

Design matrices for likelihood slices.
pfilter

Particle filter
particles-mif

Generate particles from the user-specified distribution.
dmeasure-pomp

Evaluate the probability density of observations given underlying states in a partially-observed Markov process
dprocess-pomp

Evaluate the probability density of state transitions in a Markov process
simulate-pomp

Running simulations of a partially-observed Markov process
blowflies

Model for Nicholson's blowflies.
pompBuilder

Write, compile, link, and build a pomp object using native codes
profileDesign

Design matrices for likelihood profile calculations.
pomp-methods

Methods of the "pomp" class
pomp-package

Partially-observed Markov processes
sir

SIR models.
nlf

Fit Model to Data Using Nonlinear Forecasting (NLF)
bsmc

Liu and West Bayesian Particle Filter
B-splines

B-spline bases
pmcmc-methods

Methods of the "pmcmc" class
ou2

Two-dimensional discrete-time Ornstein-Uhlenbeck process
abc-methods

Methods of the "abc" class
probe

Probe a partially-observed Markov process.
sannbox

Simulated annealing with box constraints.
pomp

Partially-observed Markov process object.
spect

Power spectrum computation for partially-observed Markov processes.
mif-methods

Methods of the "mif" class
plugins

Plug-ins for dynamical models based on stochastic Euler algorithms
rmeasure-pomp

Simulate the measurement model of a partially-observed Markov process
logmeanexp

The log-mean-exp trick
trajectory

Compute trajectories of the deterministic skeleton.
rw2

Two-dimensional random-walk process
dacca

Model of cholera transmission for historic Bengal.
prior-pomp

Evaluate or simulate from the prior probability density
probed.pomp-methods

Methods of the "probed.pomp", "probe.matched.pomp", "spect.pomp", and "spect.matched.pomp" classes
ricker

Ricker model with Poisson observations.