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pomp (version 0.43-4)
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
2,109
Version
0.43-4
License
GPL (>= 2)
Maintainer
Aaron King
Last Published
August 21st, 2012
Functions in pomp (0.43-4)
Search all functions
nlf
Fit Model to Data Using Nonlinear Forecasting (NLF)
dacca
Model of cholera transmission for historic Bengal.
sir
SIR models.
B-splines
B-spline bases
pmcmc-methods
Methods of the "pmcmc" class
probed.pomp-methods
Methods of the "probed.pomp", "probe.matched.pomp", "spect.pomp", and "spect.matched.pomp" classes
eulermultinom
Euler-multinomial death process
pomp
Partially-observed Markov process object.
sannbox
Simulated annealing with box constraints.
pfilter
Particle filter
pompBuilder
Write, compile, link, and build a pomp object using native codes
sobol
Sobol' low-discrepancy sequence
pfilter-methods
Methods of the "pfilterd.pomp" class
rprocess-pomp
Simulate the process model of a partially-observed Markov process
bsmc
Liu and West Bayesian Particle Filter
spect
Power spectrum computation for partially-observed Markov processes.
simulate-pomp
Running simulations of a partially-observed Markov process
rw2
Two-dimensional random-walk process
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
blowflies
Model for Nicholson's blowflies.
pomp-package
Partially-observed Markov processes
pomp-fun
Definition and methods of the "pomp.fun" class
mif
The MIF algorithm
pmcmc
The PMCMC algorithm
init.state-pomp
Return a matrix of initial conditions given a vector of parameters and an initial time.
ricker
Ricker model with Poisson observations.
mif-methods
Methods of the "mif" class
gompertz
Gompertz model with log-normal observations.
basic.probes
Some probes for partially-observed Markov processes
mif-class
The "mif" class
dprocess-pomp
Evaluate the probability density of state transitions in a Markov process
skeleton-pomp
Evaluate the deterministic skeleton at the given points in state space.
parmat
Create a matrix of parameters
sliceDesign
Design matrices for likelihood slices.
probe
Probe a partially-observed Markov process.
pomp-methods
Methods of the "pomp" class
rmeasure-pomp
Simulate the measurement model of a partially-observed Markov process
verhulst
Simple Verhulst-Pearl (logistic) model.
pomp-class
Partially-observed Markov process class
plugins
Plug-ins for dynamical models based on stochastic Euler algorithms
trajectory
Compute trajectories of the deterministic skeleton.
profileDesign
Design matrices for likelihood profile calculations.
traj.match
Trajectory matching
LondonYorke
Historical childhood disease incidence data
ou2
Two-dimensional discrete-time Ornstein-Uhlenbeck process