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pomp (version 0.34-2)
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
2,109
Version
0.34-2
License
GPL (>= 2)
Maintainer
Aaron King
Last Published
October 14th, 2010
Functions in pomp (0.34-2)
Search all functions
ou2
Two-dimensional Ornstein-Uhlenbeck process
particles-mif
Generate particles from the user-specified distribution.
Euler-multinomial models
Euler-multinomial models
mif-class
The "mif" class
mif
The MIF algorithm
probed.pomp-class
The "probed.pomp" and "probe.matched.pomp" classes
pomp-fun
Definition and methods of the "pomp.fun" class
ricker
Ricker model with Poisson observations.
pmcmc
The PMCMC algorithm
rmeasure-pomp
Simulate the measurement model of a partially-observed Markov process
verhulst
Simple Verhulst-Pearl (logistic) model.
traj.match
Trajectory matching
trajectory
Compute trajectories of the determinstic skeleton.
pomp-class
Partially-observed Markov process class
pmcmc-class
The "pmcmc" class
mif-methods
Methods of the "mif" class
pomp-methods
Methods of the "pomp" class
slice.design
Design matrices for likelihood slices.
dprocess-pomp
Evaluate the probability density of state transitions in a Markov process
pmcmc-methods
Methods of the "pmcmc" class
pfilter
Particle filter
basic.probes
Some probes for partially-observed Markov processes
pomp
Partially-observed Markov process object.
profile.design
Design matrices for likelihood profile calculations.
simulate-pomp
Running simulations of a partially-observed Markov process
sir
Seasonal SIR model implemented using two stochastic simulation algorithms.
bsmc
Liu and West Bayesian Particle Filter
LondonYorke
Historical childhood disease incidence data
rw2
Two-dimensional random-walk process
B-splines
B-spline bases
pomp-package
Partially-observed Markov processes
dacca
Model of cholera transmission for historic Bengal.
init.state-pomp
Return a matrix of initial conditions given a vector of parameters and an initial time.
sobol
Sobol' low-discrepancy sequence
plugins
Plug-ins for dynamical models based on stochastic Euler algorithms
rprocess-pomp
Simulate the process model of a partially-observed Markov process
spect
Power spectrum computation for partially-observed Markov processes.
dmeasure-pomp
Evaluate the probability density of observations given underlying states in a partially-observed Markov process
spect.pomp-class
The "spect.pomp" and "spect.matched.pomp" classes
skeleton-pomp
Evaluate the deterministic skeleton at the given points in state space.
probe
Probe a partially-observed Markov process.
nlf
Fit Model to Data Using Nonlinear Forecasting (NLF)
probed.pomp-methods
Methods of the "probed.pomp", "probe.matched.pomp", "spect.pomp", and "spect.matched.pomp" classes