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A B C D E F G H I K L M N O P R S T U V W
| pomp-package | Inference for partially observed Markov processes |
| abc | Approximate Bayesian computation |
| abc-data.frame | Approximate Bayesian computation |
| abc-method | Approximate Bayesian computation |
| abc-pomp | Approximate Bayesian computation |
| accumulators | accumulators |
| accumvars | accumulators |
| as-csnippet | C snippets |
| as-method | Coerce to data frame |
| as-method | C snippets |
| as.data.frame | Coerce to data frame |
| as.data.frame.abcList | Coerce to data frame |
| as.data.frame.bsmcd_pomp | Coerce to data frame |
| as.data.frame.kalmand_pomp | Coerce to data frame |
| as.data.frame.mif2List | Coerce to data frame |
| as.data.frame.pfilterd_pomp | Coerce to data frame |
| as.data.frame.pfilterList | Coerce to data frame |
| as.data.frame.pmcmcList | Coerce to data frame |
| as.data.frame.pomp | Coerce to data frame |
| as.data.frame.pompList | Coerce to data frame |
| as.data.frame.probed_pomp | Coerce to data frame |
| bake | Bake, stew, and freeze |
| basic_probes | Useful probes for partially-observed Markov processes |
| blowflies | Nicholson's blowflies. |
| blowflies1 | Nicholson's blowflies. |
| blowflies2 | Nicholson's blowflies. |
| bsflu | Influenza outbreak in a boarding school |
| bsmc2 | The Liu and West Bayesian particle filter |
| bsmc2-data.frame | 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 |
| bsplines | B-spline bases |
| coef | Extract, set, or alter coefficients |
| coef-listie | Extract, set, or alter coefficients |
| coef-method | Extract, set, or alter coefficients |
| coef-objfun | Extract, set, or alter coefficients |
| coef-pomp | Extract, set, or alter coefficients |
| coef<- | Extract, set, or alter coefficients |
| coef<--method | Extract, set, or alter coefficients |
| coef<--pomp | Extract, set, or alter coefficients |
| coerce-method | Coerce to data frame |
| coerce-method | C snippets |
| coerce-method | Probes (AKA summary statistics) |
| coerce-method | Power spectrum |
| coerce-objfun-data.frame | Coerce to data frame |
| coerce-pomp-data.frame | Coerce to data frame |
| coerce-probe_match_objfun-probed_pomp | Probes (AKA summary statistics) |
| coerce-spect_match_objfun-spectd_pomp | Power spectrum |
| cond.logLik | Conditional log likelihood |
| cond.logLik-bsmcd_pomp | Conditional log likelihood |
| cond.logLik-kalmand_pomp | Conditional log likelihood |
| cond.logLik-method | Conditional log likelihood |
| cond.logLik-pfilterd_pomp | Conditional log likelihood |
| continue | Continue an iterative calculation |
| continue-abcd_pomp | Continue an iterative calculation |
| continue-method | Continue an iterative calculation |
| continue-mif2d_pomp | Continue an iterative calculation |
| continue-pmcmcd_pomp | Continue an iterative calculation |
| covariate_table | Covariates |
| covariate_table-character-method | Covariates |
| covariate_table-method | Covariates |
| covariate_table-numeric-method | Covariates |
| covmat | Estimate a covariance matrix from algorithm traces |
| covmat-abcd_pomp | Estimate a covariance matrix from algorithm traces |
| covmat-abcList | Estimate a covariance matrix from algorithm traces |
| covmat-method | Estimate a covariance matrix from algorithm traces |
| covmat-pmcmcd_pomp | Estimate a covariance matrix from algorithm traces |
| covmat-pmcmcList | Estimate a covariance matrix from algorithm traces |
| covmat-probed_pomp | Estimate a covariance matrix from algorithm traces |
| Csnippet | C snippets |
| Csnippet-class | C snippets |
| dacca | Model of cholera transmission for historic Bengal. |
| design | Design matrices for pomp calculations |
| deulermultinom | Probability distributions |
| discrete_time | The latent state process simulator |
| distributions | Probability distributions |
| dmeasure | dmeasure |
| dmeasure-method | dmeasure |
| dmeasure-pomp | dmeasure |
| dmeasure_spec | The measurement model density |
| dprior | dprior |
| dprior-method | dprior |
| dprior-pomp | dprior |
| dprocess | dprocess |
| dprocess-method | dprocess |
| dprocess-pomp | dprocess |
| dprocess_spec | The latent state process density |
| eakf | Ensemble Kalman filters |
| eakf-data.frame | Ensemble Kalman filters |
| eakf-method | Ensemble Kalman filters |
| eakf-pomp | Ensemble Kalman filters |
| ebola | Ebola outbreak, West Africa, 2014-2016 |
| ebolaModel | Ebola outbreak, West Africa, 2014-2016 |
| ebolaWA2014 | Ebola outbreak, West Africa, 2014-2016 |
| eff.sample.size | Effective sample size |
| eff.sample.size-bsmcd_pomp | Effective sample size |
| eff.sample.size-method | Effective sample size |
| eff.sample.size-pfilterd_pomp | Effective sample size |
| enkf | Ensemble Kalman filters |
| enkf-data.frame | Ensemble Kalman filters |
| enkf-method | Ensemble Kalman filters |
| enkf-pomp | Ensemble Kalman filters |
| euler | The latent state process simulator |
| ewcitmeas | Historical childhood disease incidence data |
| ewmeas | Historical childhood disease incidence data |
| expit | Transformations |
| filter.mean | Filtering mean |
| filter.mean-kalmand_pomp | Filtering mean |
| filter.mean-method | Filtering mean |
| filter.mean-pfilterd_pomp | Filtering mean |
| filter.traj | Filtering trajectories |
| filter.traj-method | Filtering trajectories |
| filter.traj-pfilterd_pomp | Filtering trajectories |
| filter.traj-pfilterList | Filtering trajectories |
| filter.traj-pmcmcd_pomp | Filtering trajectories |
| filter.traj-pmcmcList | Filtering trajectories |
| flow | Flow of a deterministic model |
| flow-method | Flow of a deterministic model |
| flow-pomp | Flow of a deterministic model |
| Forecast | Forecast mean |
| forecast | Forecast mean |
| forecast-kalmand_pomp | Forecast mean |
| forecast-method | Forecast mean |
| freeze | Bake, stew, and freeze |
| gillespie | The latent state process simulator |
| gillespie_hl | The latent state process simulator |
| gompertz | Gompertz model with log-normal observations. |
| hitch | Hitching C snippets and R functions to pomp_fun objects |
| inv_log_barycentric | Transformations |
| kalman | Ensemble Kalman filters |
| logit | Transformations |
| logLik | Log likelihood |
| logLik-bsmcd_pomp | Log likelihood |
| logLik-kalmand_pomp | Log likelihood |
| logLik-method | Log likelihood |
| logLik-nlf_objfun | Log likelihood |
| logLik-objfun | Log likelihood |
| logLik-pfilterd_pomp | Log likelihood |
| logLik-pmcmcd_pomp | Log likelihood |
| logLik-probed_pomp | Log likelihood |
| logLik-spect_match_objfun | Log likelihood |
| logmeanexp | The log-mean-exp trick |
| log_barycentric | Transformations |
| LondonYorke | Historical childhood disease incidence data |
| map | The deterministic skeleton of a model |
| measles | Historical childhood disease incidence data |
| melt-method | Coerce to data frame |
| mif2 | Iterated filtering: maximum likelihood by iterated, perturbed Bayes maps |
| mif2-data.frame | Iterated filtering: maximum likelihood by iterated, perturbed Bayes maps |
| mif2-method | Iterated filtering: maximum likelihood by iterated, perturbed Bayes maps |
| mif2-mif2d_pomp | Iterated filtering: maximum likelihood by iterated, perturbed Bayes maps |
| mif2-pfilterd_pomp | Iterated filtering: maximum likelihood by iterated, perturbed Bayes maps |
| mif2-pomp | Iterated filtering: maximum likelihood by iterated, perturbed Bayes maps |
| mvn.diag.rw | MCMC proposal distributions |
| mvn.rw | MCMC proposal distributions |
| mvn.rw.adaptive | MCMC proposal distributions |
| nlf | Nonlinear forecasting |
| nlf_objfun | Nonlinear forecasting |
| nlf_objfun-data.frame | Nonlinear forecasting |
| nlf_objfun-method | Nonlinear forecasting |
| nlf_objfun-nlf_objfun | Nonlinear forecasting |
| nlf_objfun-pomp | Nonlinear forecasting |
| obs | obs |
| obs-method | obs |
| obs-pomp | obs |
| onestep | The latent state process simulator |
| ou2 | Two-dimensional discrete-time Ornstein-Uhlenbeck process |
| parameter_trans | Parameter transformations |
| parameter_trans-character,character | Parameter transformations |
| parameter_trans-Csnippet,Csnippet | Parameter transformations |
| parameter_trans-function,function | Parameter transformations |
| parameter_trans-method | Parameter transformations |
| parameter_trans-missing,missing | Parameter transformations |
| parmat | Create a matrix of parameters |
| partrans | partrans |
| partrans-method | partrans |
| partrans-pomp | partrans |
| parus | Parus major population dynamics |
| periodic.bspline.basis | B-spline bases |
| pfilter | Particle filter |
| pfilter-data.frame | Particle filter |
| pfilter-method | Particle filter |
| pfilter-objfun | Particle filter |
| pfilter-pfilterd_pomp | Particle filter |
| pfilter-pomp | Particle filter |
| pfilterd_pomp | Particle filter |
| pfilterd_pomp-class | Particle filter |
| plot | Plotting |
| plot-Abc | Plotting |
| plot-bsmcd_pomp | Plotting |
| plot-method | Plotting |
| plot-Mif2 | Plotting |
| plot-pfilterd_pomp | Plotting |
| plot-Pmcmc | Plotting |
| plot-pomp | Plotting |
| plot-probed_pomp | Plotting |
| plot-probe_match_objfun | Plotting |
| plot-spectd_pomp | Plotting |
| plot-spect_match_objfun | Plotting |
| pmcmc | The particle Markov chain Metropolis-Hastings algorithm |
| pmcmc-data.frame | The particle Markov chain Metropolis-Hastings algorithm |
| pmcmc-method | The particle Markov chain Metropolis-Hastings algorithm |
| pmcmc-pfilterd_pomp | The particle Markov chain Metropolis-Hastings algorithm |
| pmcmc-pmcmcd_pomp | The particle Markov chain Metropolis-Hastings algorithm |
| pmcmc-pomp | The particle Markov chain Metropolis-Hastings algorithm |
| pomp | Constructor of the basic pomp object |
| pomp,package | Inference for partially observed Markov processes |
| pred.mean | Prediction mean |
| pred.mean-kalmand_pomp | Prediction mean |
| pred.mean-method | Prediction mean |
| pred.mean-pfilterd_pomp | Prediction mean |
| pred.var | Prediction variance |
| pred.var-method | Prediction variance |
| pred.var-pfilterd_pomp | Prediction variance |
| Print methods | |
| print-method | Print methods |
| prior_spec | prior specification |
| probe | Probes (AKA summary statistics) |
| probe-data.frame | Probes (AKA summary statistics) |
| probe-method | Probes (AKA summary statistics) |
| probe-objfun | Probes (AKA summary statistics) |
| probe-pomp | Probes (AKA summary statistics) |
| probe-probed_pomp | Probes (AKA summary statistics) |
| probe-probe_match_obfjun | Probes (AKA summary statistics) |
| probe.acf | Useful probes for partially-observed Markov processes |
| probe.ccf | Useful probes for partially-observed Markov processes |
| probe.marginal | Useful probes for partially-observed Markov processes |
| probe.match | Probe matching |
| probe.mean | Useful probes for partially-observed Markov processes |
| probe.median | Useful probes for partially-observed Markov processes |
| probe.nlar | Useful probes for partially-observed Markov processes |
| probe.period | Useful probes for partially-observed Markov processes |
| probe.quantile | Useful probes for partially-observed Markov processes |
| probe.sd | Useful probes for partially-observed Markov processes |
| probe.var | Useful probes for partially-observed Markov processes |
| probe_objfun | Probe matching |
| probe_objfun-data.frame | Probe matching |
| probe_objfun-method | Probe matching |
| probe_objfun-pomp | Probe matching |
| probe_objfun-probed_pomp | Probe matching |
| probe_objfun-probe_match_objfun | Probe matching |
| profileDesign | Design matrices for pomp calculations |
| proposals | MCMC proposal distributions |
| reulermultinom | Probability distributions |
| rgammawn | Probability distributions |
| ricker | Ricker model with Poisson observations. |
| rinit | rinit |
| rinit-method | rinit |
| rinit-pomp | rinit |
| rinit_spec | The initial-state distribution |
| rmeasure | rmeasure |
| rmeasure-method | rmeasure |
| rmeasure-pomp | rmeasure |
| rmeasure_spec | The measurement-model simulator |
| rprior | rprior |
| rprior-method | rprior |
| rprior-pomp | rprior |
| rprocess | rprocess |
| rprocess-method | rprocess |
| rprocess-pomp | rprocess |
| rprocess_spec | The latent state process simulator |
| runifDesign | Design matrices for pomp calculations |
| rw.sd | rw.sd |
| rw2 | Two-dimensional random-walk process |
| sannbox | Simulated annealing with box constraints. |
| simulate | Simulations of a partially-observed Markov process |
| simulate-data.frame | Simulations of a partially-observed Markov process |
| simulate-method | Simulations of a partially-observed Markov process |
| simulate-missing | Simulations of a partially-observed Markov process |
| simulate-objfun | Simulations of a partially-observed Markov process |
| simulate-pomp | Simulations of a partially-observed Markov process |
| sir | Compartmental epidemiological models |
| sir2 | Compartmental epidemiological models |
| sir_models | Compartmental epidemiological models |
| skeleton | skeleton |
| skeleton-method | skeleton |
| skeleton-pomp | skeleton |
| skeleton_spec | The deterministic skeleton of a model |
| sliceDesign | Design matrices for pomp calculations |
| sobolDesign | Design matrices for pomp calculations |
| spect | Power spectrum |
| spect-data.frame | Power spectrum |
| spect-method | Power spectrum |
| spect-objfun | Power spectrum |
| spect-pomp | Power spectrum |
| spect-spectd_pomp | Power spectrum |
| spect-spect_match_objfun | Power spectrum |
| spect.match | Spectrum matching |
| spect_objfun | Spectrum matching |
| spect_objfun-data.frame | Spectrum matching |
| spect_objfun-method | Spectrum matching |
| spect_objfun-pomp | Spectrum matching |
| spect_objfun-spectd_pomp | Spectrum matching |
| spect_objfun-spect_match_objfun | Spectrum matching |
| spy | Spy |
| spy-method | Spy |
| states | Latent states |
| states-method | Latent states |
| states-pomp | Latent states |
| stew | Bake, stew, and freeze |
| summary | Summary methods |
| summary-method | Summary methods |
| summary-objfun | Summary methods |
| summary-probed_pomp | Summary methods |
| summary-spectd_pomp | Summary methods |
| time | Methods to manipulate the obseration times |
| time-method | Methods to manipulate the obseration times |
| time-pomp | Methods to manipulate the obseration times |
| time<- | Methods to manipulate the obseration times |
| time<--method | Methods to manipulate the obseration times |
| time<--pomp | Methods to manipulate the obseration times |
| timezero | The zero time |
| timezero-method | The zero time |
| timezero-pomp | The zero time |
| timezero<- | The zero time |
| timezero<--method | The zero time |
| timezero<--pomp | The zero time |
| traces | Traces |
| traces-Abc | Traces |
| traces-abcd_pomp | Traces |
| traces-abcList | Traces |
| traces-method | Traces |
| traces-Mif2 | Traces |
| traces-mif2d_pomp | Traces |
| traces-mif2List | Traces |
| traces-Pmcmc | Traces |
| traces-pmcmcd_pomp | Traces |
| traces-pmcmcList | Traces |
| traj.match | Trajectory matching |
| trajectory | Trajectory of a deterministic model |
| trajectory-method | Trajectory of a deterministic model |
| trajectory-pomp | Trajectory of a deterministic model |
| trajectory-traj_match_objfun | Trajectory of a deterministic model |
| traj_objfun | Trajectory matching |
| traj_objfun-data.frame | Trajectory matching |
| traj_objfun-method | Trajectory matching |
| traj_objfun-pomp | Trajectory matching |
| traj_objfun-traj_match_objfun | Trajectory matching |
| transformations | Transformations |
| userdata | Facilities for making additional information to basic components |
| vectorfield | The deterministic skeleton of a model |
| verhulst | Verhulst-Pearl model |
| window | Window |
| window-method | Window |
| window-pomp | Window |
| workhorses | Workhorse functions for the 'pomp' algorithms. |