Statistical Inference for Partially Observed Markov Processes


[Up] [Top]

Documentation for package ‘pomp’ version 1.7

Help Pages

A B C D E F G I K L M N O P R S T V W misc

pomp-package Inference for partially observed Markov processes

-- A --

ABC Estimation by approximate Bayesian computation (ABC)
abc Estimation by approximate Bayesian computation (ABC)
abc-abc Estimation by approximate Bayesian computation (ABC)
abc-class Estimation by approximate Bayesian computation (ABC)
abc-method Estimation by approximate Bayesian computation (ABC)
abc-methods Estimation by approximate Bayesian computation (ABC)
abc-pomp Estimation by approximate Bayesian computation (ABC)
abc-probed.pomp Estimation by approximate Bayesian computation (ABC)
abcList-class Estimation by approximate Bayesian computation (ABC)
accumulator variables Constructor of the basic pomp object
as-method Ensemble Kalman filters
as-method Particle filter
as-method Functions for manipulating, displaying, and extracting information from objects of the 'pomp' class
as-method Probe a partially-observed Markov process by computing summary statistics and the synthetic likelihood.
as.data.frame.kalmand.pomp Ensemble Kalman filters
as.data.frame.pfilterd.pomp Particle filter
as.data.frame.pomp Functions for manipulating, displaying, and extracting information from objects of the 'pomp' class

-- B --

bake Tools for reproducible computations.
basic.probes Some useful probes for partially-observed Markov processes
Bayesian sequential Monte Carlo The Liu and West Bayesian particle filter
bbs Compartmental epidemiological models
blowflies Model for Nicholson's blowflies.
blowflies1 Model for Nicholson's blowflies.
blowflies2 Model for Nicholson's blowflies.
bsmc The Liu and West Bayesian particle filter
bsmc-method The Liu and West Bayesian particle filter
bsmc-pomp The Liu and West Bayesian particle filter
bsmc2 The Liu and West Bayesian particle filter
bsmc2-method The Liu and West Bayesian particle filter
bsmc2-pomp The Liu and West Bayesian particle filter
bspline.basis B-spline bases

-- C --

c-abc Estimation by approximate Bayesian computation (ABC)
c-abcList Estimation by approximate Bayesian computation (ABC)
c-method Estimation by approximate Bayesian computation (ABC)
c-method Maximum likelihood by iterated filtering
c-method IF2: Maximum likelihood by iterated, perturbed Bayes maps
c-method The particle Markov chain Metropolis-Hastings algorithm
c-mif Maximum likelihood by iterated filtering
c-mif2d.pomp IF2: Maximum likelihood by iterated, perturbed Bayes maps
c-mif2List IF2: Maximum likelihood by iterated, perturbed Bayes maps
c-mifList Maximum likelihood by iterated filtering
c-pmcmc The particle Markov chain Metropolis-Hastings algorithm
c-pmcmcList The particle Markov chain Metropolis-Hastings algorithm
coef-method Maximum likelihood by iterated filtering
coef-method IF2: Maximum likelihood by iterated, perturbed Bayes maps
coef-method Functions for manipulating, displaying, and extracting information from objects of the 'pomp' class
coef-pomp Functions for manipulating, displaying, and extracting information from objects of the 'pomp' class
coef.rec-mif2List IF2: Maximum likelihood by iterated, perturbed Bayes maps
coef.rec-mifList Maximum likelihood by iterated filtering
coef<- Functions for manipulating, displaying, and extracting information from objects of the 'pomp' class
coef<--method Functions for manipulating, displaying, and extracting information from objects of the 'pomp' class
coef<--pomp Functions for manipulating, displaying, and extracting information from objects of the 'pomp' class
coerce-method Ensemble Kalman filters
coerce-method Particle filter
coerce-method Functions for manipulating, displaying, and extracting information from objects of the 'pomp' class
coerce-method Probe a partially-observed Markov process by computing summary statistics and the synthetic likelihood.
cond.logLik Particle filter
cond.logLik-kalmand.pomp Ensemble Kalman filters
cond.logLik-method Ensemble Kalman filters
cond.logLik-method Particle filter
cond.logLik-pfilterd.pomp Particle filter
continue IF2: Maximum likelihood by iterated, perturbed Bayes maps
continue-abc Estimation by approximate Bayesian computation (ABC)
continue-method Estimation by approximate Bayesian computation (ABC)
continue-method Maximum likelihood by iterated filtering
continue-method IF2: Maximum likelihood by iterated, perturbed Bayes maps
continue-method The particle Markov chain Metropolis-Hastings algorithm
continue-mif Maximum likelihood by iterated filtering
continue-mif2d.pomp IF2: Maximum likelihood by iterated, perturbed Bayes maps
continue-pmcmc The particle Markov chain Metropolis-Hastings algorithm
conv.rec Maximum likelihood by iterated filtering
conv.rec-abc Estimation by approximate Bayesian computation (ABC)
conv.rec-abcList Estimation by approximate Bayesian computation (ABC)
conv.rec-method Estimation by approximate Bayesian computation (ABC)
conv.rec-method Maximum likelihood by iterated filtering
conv.rec-method IF2: Maximum likelihood by iterated, perturbed Bayes maps
conv.rec-method The particle Markov chain Metropolis-Hastings algorithm
conv.rec-mif Maximum likelihood by iterated filtering
conv.rec-mif2d.pomp IF2: Maximum likelihood by iterated, perturbed Bayes maps
conv.rec-mif2List IF2: Maximum likelihood by iterated, perturbed Bayes maps
conv.rec-mifList Maximum likelihood by iterated filtering
conv.rec-pmcmc The particle Markov chain Metropolis-Hastings algorithm
conv.rec-pmcmcList The particle Markov chain Metropolis-Hastings algorithm
covmat Estimation by approximate Bayesian computation (ABC)
covmat-abc Estimation by approximate Bayesian computation (ABC)
covmat-abcList Estimation by approximate Bayesian computation (ABC)
covmat-method Estimation by approximate Bayesian computation (ABC)
covmat-method The particle Markov chain Metropolis-Hastings algorithm
covmat-pmcmc The particle Markov chain Metropolis-Hastings algorithm
covmat-pmcmcList The particle Markov chain Metropolis-Hastings algorithm
Csnippet Constructor of the basic pomp object
Csnippet-class Constructor of the basic pomp object

-- D --

dacca Model of cholera transmission for historic Bengal.
deulermultinom The Euler-multinomial distributions and Gamma white-noise processes
discrete.time.sim Constructor of the basic pomp object
dmeasure pomp low-level interface
dmeasure-method pomp low-level interface
dmeasure-pomp pomp low-level interface
dprior pomp low-level interface
dprior-method pomp low-level interface
dprior-pomp pomp low-level interface
dprocess pomp low-level interface
dprocess-method pomp low-level interface
dprocess-pomp pomp low-level interface

-- E --

eakf Ensemble Kalman filters
eakf-method Ensemble Kalman filters
eakf-pomp Ensemble Kalman filters
eff.sample.size Particle filter
eff.sample.size-method Particle filter
eff.sample.size-pfilterd.pomp Particle filter
enkf Ensemble Kalman filters
enkf-method Ensemble Kalman filters
enkf-pomp Ensemble Kalman filters
ensembe Kalman filter Ensemble Kalman filters
ensemble adjustment Kalman filter Ensemble Kalman filters
euler.sim Constructor of the basic pomp object
euler.sir Compartmental epidemiological models
eulermultinom The Euler-multinomial distributions and Gamma white-noise processes
ewcitmeas Historical childhood disease incidence data
ewmeas Historical childhood disease incidence data
Example pomp models Examples of the construction of POMP models

-- F --

filter.mean Particle filter
filter.mean-kalmand.pomp Ensemble Kalman filters
filter.mean-method Ensemble Kalman filters
filter.mean-method Particle filter
filter.mean-pfilterd.pomp Particle filter
filter.traj Particle filter
filter.traj-method Particle filter
filter.traj-method The particle Markov chain Metropolis-Hastings algorithm
filter.traj-pfilterd.pomp Particle filter
filter.traj-pmcmc The particle Markov chain Metropolis-Hastings algorithm
filter.traj-pmcmcList The particle Markov chain Metropolis-Hastings algorithm
freeze Tools for reproducible computations.

-- G --

gillespie.sim Constructor of the basic pomp object
gillespie.sir Compartmental epidemiological models
gompertz Gompertz model with log-normal observations.

-- I --

init.state pomp low-level interface
init.state-method pomp low-level interface
init.state-pomp pomp low-level interface

-- K --

kalmand.pomp Ensemble Kalman filters
kalmand.pomp-class Ensemble Kalman filters
kleap.sim Constructor of the basic pomp object

-- L --

logLik-kalmand.pomp Ensemble Kalman filters
logLik-method Ensemble Kalman filters
logLik-method Parameter estimation my maximum simulated quasi-likelihood (nonlinear forecasting)
logLik-method Particle filter
logLik-method The particle Markov chain Metropolis-Hastings algorithm
logLik-method Probe a partially-observed Markov process by computing summary statistics and the synthetic likelihood.
logLik-method Parameter estimation by fitting the trajectory of a model's deterministic skeleton to data
logLik-nlfd.pomp Parameter estimation my maximum simulated quasi-likelihood (nonlinear forecasting)
logLik-pfilterd.pomp Particle filter
logLik-pmcmc The particle Markov chain Metropolis-Hastings algorithm
logLik-probed.pomp Probe a partially-observed Markov process by computing summary statistics and the synthetic likelihood.
logLik-traj.matched.pomp Parameter estimation by fitting the trajectory of a model's deterministic skeleton to data
logmeanexp The log-mean-exp trick
LondonYorke Historical childhood disease incidence data

-- M --

MCMC proposal distributions MCMC proposal distributions
MCMC proposal functions MCMC proposal distributions
mif Maximum likelihood by iterated filtering
mif-class Maximum likelihood by iterated filtering
mif-method Maximum likelihood by iterated filtering
mif-methods Maximum likelihood by iterated filtering
mif-mif Maximum likelihood by iterated filtering
mif-pfilterd.pomp Maximum likelihood by iterated filtering
mif-pomp Maximum likelihood by iterated filtering
mif2 IF2: Maximum likelihood by iterated, perturbed Bayes maps
mif2-method IF2: Maximum likelihood by iterated, perturbed Bayes maps
mif2-mif2d.pomp IF2: Maximum likelihood by iterated, perturbed Bayes maps
mif2-pfilterd.pomp IF2: Maximum likelihood by iterated, perturbed Bayes maps
mif2-pomp IF2: Maximum likelihood by iterated, perturbed Bayes maps
mif2d.pomp-class IF2: Maximum likelihood by iterated, perturbed Bayes maps
mif2d.pomp-methods IF2: Maximum likelihood by iterated, perturbed Bayes maps
mif2List-class IF2: Maximum likelihood by iterated, perturbed Bayes maps
mifList-class Maximum likelihood by iterated filtering
mvn.diag.rw MCMC proposal distributions
mvn.rw MCMC proposal distributions
mvn.rw.adaptive MCMC proposal distributions

-- N --

nlf Parameter estimation my maximum simulated quasi-likelihood (nonlinear forecasting)
nlf-method Parameter estimation my maximum simulated quasi-likelihood (nonlinear forecasting)
nlf-nlfd.pomp Parameter estimation my maximum simulated quasi-likelihood (nonlinear forecasting)
nlf-pomp Parameter estimation my maximum simulated quasi-likelihood (nonlinear forecasting)
nlfd.pomp-class Parameter estimation my maximum simulated quasi-likelihood (nonlinear forecasting)
Nonlinear forecasting Parameter estimation my maximum simulated quasi-likelihood (nonlinear forecasting)

-- O --

obs Functions for manipulating, displaying, and extracting information from objects of the 'pomp' class
obs-method Functions for manipulating, displaying, and extracting information from objects of the 'pomp' class
obs-pomp Functions for manipulating, displaying, and extracting information from objects of the 'pomp' class
onestep.dens Constructor of the basic pomp object
onestep.sim Constructor of the basic pomp object
ou2 Two-dimensional discrete-time Ornstein-Uhlenbeck process

-- P --

parmat Create a matrix of parameters
particle filter Particle filter
partrans Functions for manipulating, displaying, and extracting information from objects of the 'pomp' class
partrans-method Functions for manipulating, displaying, and extracting information from objects of the 'pomp' class
partrans-pomp Functions for manipulating, displaying, and extracting information from objects of the 'pomp' class
periodic.bspline.basis B-spline bases
pfilter Particle filter
pfilter-method Particle filter
pfilter-pfilterd.pomp Particle filter
pfilter-pomp Particle filter
pfilterd.pomp Particle filter
pfilterd.pomp-class Particle filter
plot-abc Estimation by approximate Bayesian computation (ABC)
plot-abcList Estimation by approximate Bayesian computation (ABC)
plot-bsmcd.pomp The Liu and West Bayesian particle filter
plot-method Estimation by approximate Bayesian computation (ABC)
plot-method The Liu and West Bayesian particle filter
plot-method Maximum likelihood by iterated filtering
plot-method IF2: Maximum likelihood by iterated, perturbed Bayes maps
plot-method The particle Markov chain Metropolis-Hastings algorithm
plot-method Functions for manipulating, displaying, and extracting information from objects of the 'pomp' class
plot-method Probe a partially-observed Markov process by computing summary statistics and the synthetic likelihood.
plot-mif Maximum likelihood by iterated filtering
plot-mif2d.pomp IF2: Maximum likelihood by iterated, perturbed Bayes maps
plot-mif2List IF2: Maximum likelihood by iterated, perturbed Bayes maps
plot-mifList Maximum likelihood by iterated filtering
plot-pmcmc The particle Markov chain Metropolis-Hastings algorithm
plot-pmcmcList The particle Markov chain Metropolis-Hastings algorithm
plot-pomp Functions for manipulating, displaying, and extracting information from objects of the 'pomp' class
plot-probe.matched.pomp Probe a partially-observed Markov process by computing summary statistics and the synthetic likelihood.
plot-probed.pomp Probe a partially-observed Markov process by computing summary statistics and the synthetic likelihood.
plot-spect.pomp Probe a partially-observed Markov process by computing summary statistics and the synthetic likelihood.
plug-ins Constructor of the basic pomp object
pmcmc The particle Markov chain Metropolis-Hastings algorithm
pmcmc-class The particle Markov chain Metropolis-Hastings algorithm
pmcmc-method The particle Markov chain Metropolis-Hastings algorithm
pmcmc-methods The particle Markov chain Metropolis-Hastings algorithm
pmcmc-pfilterd.pomp The particle Markov chain Metropolis-Hastings algorithm
pmcmc-pmcmc The particle Markov chain Metropolis-Hastings algorithm
pmcmc-pomp The particle Markov chain Metropolis-Hastings algorithm
pmcmcList-class The particle Markov chain Metropolis-Hastings algorithm
pomp Constructor of the basic pomp object
pomp constructor Constructor of the basic pomp object
pomp low-level interface pomp low-level interface
pomp methods Functions for manipulating, displaying, and extracting information from objects of the 'pomp' class
pomp package Inference for partially observed Markov processes
POMP simulation Simulations of a partially-observed Markov process
pomp-class Constructor of the basic pomp object
pomp-methods Functions for manipulating, displaying, and extracting information from objects of the 'pomp' class
pompExample Examples of the construction of POMP models
pompLoad pomp low-level interface
pompLoad-method pomp low-level interface
pompLoad-pomp pomp low-level interface
pompUnload pomp low-level interface
pompUnload-method pomp low-level interface
pompUnload-pomp pomp low-level interface
Power spectrum computation and matching Power spectrum computation and spectrum-matching for partially-observed Markov processes
power spectrum computation and matching Power spectrum computation and spectrum-matching for partially-observed Markov processes
pred.mean Particle filter
pred.mean-kalmand.pomp Ensemble Kalman filters
pred.mean-method Ensemble Kalman filters
pred.mean-method Particle filter
pred.mean-pfilterd.pomp Particle filter
pred.var Particle filter
pred.var-method Particle filter
pred.var-pfilterd.pomp Particle filter
print-method Functions for manipulating, displaying, and extracting information from objects of the 'pomp' class
print-pomp Functions for manipulating, displaying, and extracting information from objects of the 'pomp' class
probe Probe a partially-observed Markov process by computing summary statistics and the synthetic likelihood.
Probe functions Some useful probes for partially-observed Markov processes
probe functions Some useful probes for partially-observed Markov processes
probe-method Probe a partially-observed Markov process by computing summary statistics and the synthetic likelihood.
probe-pomp Probe a partially-observed Markov process by computing summary statistics and the synthetic likelihood.
probe-probed.pomp Probe a partially-observed Markov process by computing summary statistics and the synthetic likelihood.
probe.acf Some useful probes for partially-observed Markov processes
probe.ccf Some useful probes for partially-observed Markov processes
probe.marginal Some useful probes for partially-observed Markov processes
probe.match Probe a partially-observed Markov process by computing summary statistics and the synthetic likelihood.
probe.match-method Probe a partially-observed Markov process by computing summary statistics and the synthetic likelihood.
probe.match-pomp Probe a partially-observed Markov process by computing summary statistics and the synthetic likelihood.
probe.match-probe.matched.pomp Probe a partially-observed Markov process by computing summary statistics and the synthetic likelihood.
probe.match-probed.pomp Probe a partially-observed Markov process by computing summary statistics and the synthetic likelihood.
probe.match.objfun Probe a partially-observed Markov process by computing summary statistics and the synthetic likelihood.
probe.match.objfun-method Probe a partially-observed Markov process by computing summary statistics and the synthetic likelihood.
probe.match.objfun-pomp Probe a partially-observed Markov process by computing summary statistics and the synthetic likelihood.
probe.match.objfun-probed.pomp Probe a partially-observed Markov process by computing summary statistics and the synthetic likelihood.
probe.matched.pomp-class Probe a partially-observed Markov process by computing summary statistics and the synthetic likelihood.
probe.matched.pomp-methods Probe a partially-observed Markov process by computing summary statistics and the synthetic likelihood.
probe.mean Some useful probes for partially-observed Markov processes
probe.median Some useful probes for partially-observed Markov processes
probe.nlar Some useful probes for partially-observed Markov processes
probe.period Some useful probes for partially-observed Markov processes
probe.quantile Some useful probes for partially-observed Markov processes
probe.sd Some useful probes for partially-observed Markov processes
probe.var Some useful probes for partially-observed Markov processes
probed.pomp-class Probe a partially-observed Markov process by computing summary statistics and the synthetic likelihood.
probed.pomp-methods Probe a partially-observed Markov process by computing summary statistics and the synthetic likelihood.
Probes and synthetic likelihood Probe a partially-observed Markov process by computing summary statistics and the synthetic likelihood.
process model plug-ins Constructor of the basic pomp object
profileDesign Design matrices for pomp calculations

-- R --

reulermultinom The Euler-multinomial distributions and Gamma white-noise processes
rgammawn The Euler-multinomial distributions and Gamma white-noise processes
ricker Ricker model with Poisson observations.
rmeasure pomp low-level interface
rmeasure-method pomp low-level interface
rmeasure-pomp pomp low-level interface
rprior pomp low-level interface
rprior-method pomp low-level interface
rprior-pomp pomp low-level interface
rprocess pomp low-level interface
rprocess-method pomp low-level interface
rprocess-pomp pomp low-level interface
rw.sd IF2: Maximum likelihood by iterated, perturbed Bayes maps
rw2 Two-dimensional random-walk process

-- S --

sannbox Simulated annealing with box constraints.
sequential Monte Carlo Particle filter
show-method Functions for manipulating, displaying, and extracting information from objects of the 'pomp' class
show-pomp Functions for manipulating, displaying, and extracting information from objects of the 'pomp' class
simulate-method Simulations of a partially-observed Markov process
simulate-pomp Simulations of a partially-observed Markov process
Simulated annealing Simulated annealing with box constraints.
skeleton pomp low-level interface
skeleton-method pomp low-level interface
skeleton-pomp pomp low-level interface
sliceDesign Design matrices for pomp calculations
SMC Particle filter
sobol Design matrices for pomp calculations
sobolDesign Design matrices for pomp calculations
spect Power spectrum computation and spectrum-matching for partially-observed Markov processes
spect-method Power spectrum computation and spectrum-matching for partially-observed Markov processes
spect-pomp Power spectrum computation and spectrum-matching for partially-observed Markov processes
spect-spect.pomp Power spectrum computation and spectrum-matching for partially-observed Markov processes
spect.match Power spectrum computation and spectrum-matching for partially-observed Markov processes
spect.match-method Power spectrum computation and spectrum-matching for partially-observed Markov processes
spect.match-pomp Power spectrum computation and spectrum-matching for partially-observed Markov processes
spect.match-spect.pomp Power spectrum computation and spectrum-matching for partially-observed Markov processes
spect.matched.pomp-class Power spectrum computation and spectrum-matching for partially-observed Markov processes
spect.matched.pomp-methods Probe a partially-observed Markov process by computing summary statistics and the synthetic likelihood.
spect.pomp-class Power spectrum computation and spectrum-matching for partially-observed Markov processes
spect.pomp-methods Probe a partially-observed Markov process by computing summary statistics and the synthetic likelihood.
states Functions for manipulating, displaying, and extracting information from objects of the 'pomp' class
states-method Functions for manipulating, displaying, and extracting information from objects of the 'pomp' class
states-pomp Functions for manipulating, displaying, and extracting information from objects of the 'pomp' class
stew Tools for reproducible computations.
summary-method Probe a partially-observed Markov process by computing summary statistics and the synthetic likelihood.
summary-method Parameter estimation by fitting the trajectory of a model's deterministic skeleton to data
summary-probe.matched.pomp Probe a partially-observed Markov process by computing summary statistics and the synthetic likelihood.
summary-probed.pomp Probe a partially-observed Markov process by computing summary statistics and the synthetic likelihood.
summary-spect.matched.pomp Probe a partially-observed Markov process by computing summary statistics and the synthetic likelihood.
summary-spect.pomp Probe a partially-observed Markov process by computing summary statistics and the synthetic likelihood.
summary-traj.matched.pomp Parameter estimation by fitting the trajectory of a model's deterministic skeleton to data

-- T --

The pomp package Inference for partially observed Markov processes
time-method Functions for manipulating, displaying, and extracting information from objects of the 'pomp' class
time-pomp Functions for manipulating, displaying, and extracting information from objects of the 'pomp' class
time<- Functions for manipulating, displaying, and extracting information from objects of the 'pomp' class
time<--method Functions for manipulating, displaying, and extracting information from objects of the 'pomp' class
time<--pomp Functions for manipulating, displaying, and extracting information from objects of the 'pomp' class
timezero Functions for manipulating, displaying, and extracting information from objects of the 'pomp' class
timezero-method Functions for manipulating, displaying, and extracting information from objects of the 'pomp' class
timezero-pomp Functions for manipulating, displaying, and extracting information from objects of the 'pomp' class
timezero<- Functions for manipulating, displaying, and extracting information from objects of the 'pomp' class
timezero<--method Functions for manipulating, displaying, and extracting information from objects of the 'pomp' class
timezero<--pomp Functions for manipulating, displaying, and extracting information from objects of the 'pomp' class
traj.match Parameter estimation by fitting the trajectory of a model's deterministic skeleton to data
traj.match-method Parameter estimation by fitting the trajectory of a model's deterministic skeleton to data
traj.match-pomp Parameter estimation by fitting the trajectory of a model's deterministic skeleton to data
traj.match-traj.matched.pomp Parameter estimation by fitting the trajectory of a model's deterministic skeleton to data
traj.match.objfun Parameter estimation by fitting the trajectory of a model's deterministic skeleton to data
traj.match.objfun-method Parameter estimation by fitting the trajectory of a model's deterministic skeleton to data
traj.match.objfun-pomp Parameter estimation by fitting the trajectory of a model's deterministic skeleton to data
traj.matched.pomp-class Parameter estimation by fitting the trajectory of a model's deterministic skeleton to data
trajectory pomp low-level interface
Trajectory matching Parameter estimation by fitting the trajectory of a model's deterministic skeleton to data
trajectory-method pomp low-level interface
trajectory-pomp pomp low-level interface

-- V --

values Probe a partially-observed Markov process by computing summary statistics and the synthetic likelihood.
values-method Probe a partially-observed Markov process by computing summary statistics and the synthetic likelihood.
values-probe.matched.pomp Probe a partially-observed Markov process by computing summary statistics and the synthetic likelihood.
values-probed.pomp Probe a partially-observed Markov process by computing summary statistics and the synthetic likelihood.

-- W --

window-method Functions for manipulating, displaying, and extracting information from objects of the 'pomp' class
window-pomp Functions for manipulating, displaying, and extracting information from objects of the 'pomp' class

-- misc --

$-bsmcd.pomp The Liu and West Bayesian particle filter
$-kalmand.pomp Ensemble Kalman filters
$-method The Liu and West Bayesian particle filter
$-method Ensemble Kalman filters
$-method Parameter estimation my maximum simulated quasi-likelihood (nonlinear forecasting)
$-method Particle filter
$-method Probe a partially-observed Markov process by computing summary statistics and the synthetic likelihood.
$-method Parameter estimation by fitting the trajectory of a model's deterministic skeleton to data
$-nlfd.pomp Parameter estimation my maximum simulated quasi-likelihood (nonlinear forecasting)
$-pfilterd.pomp Particle filter
$-probe.matched.pomp Probe a partially-observed Markov process by computing summary statistics and the synthetic likelihood.
$-probed.pomp Probe a partially-observed Markov process by computing summary statistics and the synthetic likelihood.
$-traj.matched.pomp Parameter estimation by fitting the trajectory of a model's deterministic skeleton to data
[-abcList Estimation by approximate Bayesian computation (ABC)
[-method Estimation by approximate Bayesian computation (ABC)
[-method Maximum likelihood by iterated filtering
[-method IF2: Maximum likelihood by iterated, perturbed Bayes maps
[-method The particle Markov chain Metropolis-Hastings algorithm
[-mif2List IF2: Maximum likelihood by iterated, perturbed Bayes maps
[-mifList Maximum likelihood by iterated filtering
[-pmcmcList The particle Markov chain Metropolis-Hastings algorithm