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 |
Constructor of the basic pomp object |
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 |
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 |
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 |
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 |
$-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 |