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pomp (version 0.39-2)
Statistical inference for partially observed Markov processes
Description
Inference methods for partially-observed Markov processes
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Install
install.packages('pomp')
Monthly Downloads
6,668
Version
0.39-2
License
GPL (>= 2)
Maintainer
Aaron King
Last Published
September 1st, 2011
Functions in pomp (0.39-2)
Search all functions
mif-class
The "mif" class
blowflies
Model for Nicholson's blowflies.
sir
Seasonal SIR model implemented using two stochastic simulation algorithms.
ricker
Ricker model with Poisson observations.
profileDesign
Design matrices for likelihood profile calculations.
dmeasure-pomp
Evaluate the probability density of observations given underlying states in a partially-observed Markov process
B-splines
B-spline bases
eulermultinom
Euler-multinomial death process
pomp-class
Partially-observed Markov process class
particles-mif
Generate particles from the user-specified distribution.
pomp
Partially-observed Markov process object.
basic.probes
Some probes for partially-observed Markov processes
dprocess-pomp
Evaluate the probability density of state transitions in a Markov process
mif
The MIF algorithm
bsmc
Liu and West Bayesian Particle Filter
probe
Probe a partially-observed Markov process.
pomp-methods
Methods of the "pomp" class
rprocess-pomp
Simulate the process model of a partially-observed Markov process
probed.pomp-methods
Methods of the "probed.pomp", "probe.matched.pomp", "spect.pomp", and "spect.matched.pomp" classes
trajectory
Compute trajectories of the determinstic skeleton.
pmcmc
The PMCMC algorithm
pmcmc-methods
Methods of the "pmcmc" class
pfilter
Particle filter
pomp-fun
Definition and methods of the "pomp.fun" class
traj.match
Trajectory matching
rw2
Two-dimensional random-walk process
sobol
Sobol' low-discrepancy sequence
sliceDesign
Design matrices for likelihood slices.
dacca
Model of cholera transmission for historic Bengal.
skeleton-pomp
Evaluate the deterministic skeleton at the given points in state space.
gompertz
Gompertz model with log-normal observations.
spect
Power spectrum computation for partially-observed Markov processes.
nlf
Fit Model to Data Using Nonlinear Forecasting (NLF)
ou2
Two-dimensional discrete-time Ornstein-Uhlenbeck process
mif-methods
Methods of the "mif" class
verhulst
Simple Verhulst-Pearl (logistic) model.
LondonYorke
Historical childhood disease incidence data
rmeasure-pomp
Simulate the measurement model of a partially-observed Markov process
pomp-package
Partially-observed Markov processes
pfilter-methods
Methods of the "pfilterd.pomp" class
plugins
Plug-ins for dynamical models based on stochastic Euler algorithms
init.state-pomp
Return a matrix of initial conditions given a vector of parameters and an initial time.
simulate-pomp
Running simulations of a partially-observed Markov process