| 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 |
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-method |
Particle filter |
| cond.logLik-pfilterd.pomp |
Particle filter |
| continue |
Maximum likelihood by iterated filtering |
| 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 |
C code snippets for accelerating computations |
| Csnippet-class |
C code snippets for accelerating computations |
| 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. |
| plugins |
Plug-ins for state-process models |
| 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-method |
Constructor of the basic POMP object |
| pomp-methods |
Functions for manipulating, displaying, and extracting information from objects of the 'pomp' class |
| pomp-pomp |
Constructor of the basic POMP object |
| 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-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 plugins |
Plug-ins for state-process models |
| 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 |