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-method | Approximate Bayesian computation |
| accumvars | accumulator variables |
| bake | Tools for reproducible computations |
| basic_components | Basic POMP model components. |
| basic_probes | Useful probes for partially-observed Markov processes |
| betabinomial | Beta-binomial distribution |
| 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-method | The Liu and West Bayesian particle filter |
| bsplines | B-spline bases |
| bspline_basis | B-spline bases |
| c | Concatenate |
| c.Pomp | Concatenate |
| childhood_disease_data | Historical childhood disease incidence data |
| coef | Extract, set, or alter coefficients |
| coef-method | Extract, set, or alter coefficients |
| coef<- | Extract, set, or alter coefficients |
| coef<--method | Extract, set, or alter coefficients |
| compartmental_models | Compartmental epidemiological models |
| concat | Concatenate |
| cond_logLik | Conditional log likelihood |
| cond_logLik-method | Conditional log likelihood |
| continue | Continue an iterative calculation |
| continue-method | Continue an iterative calculation |
| covariates | Covariates |
| covariate_table | Covariates |
| covariate_table-method | Covariates |
| covmat | Estimate a covariance matrix from algorithm traces |
| covmat-method | Estimate a covariance matrix from algorithm traces |
| Csnippet | C snippets |
| dacca | Model of cholera transmission for historic Bengal. |
| dbetabinom | Beta-binomial distribution |
| design | Design matrices for pomp calculations |
| deulermultinom | Eulermultinomial and gamma-whitenoise distributions |
| dinit | dinit workhorse |
| dinit-method | dinit workhorse |
| dinit_spec | dinit specification |
| discrete_time | rprocess specification |
| dmeasure | dmeasure workhorse |
| dmeasure-method | dmeasure workhorse |
| dmeasure_spec | dmeasure specification |
| dprior | dprior workhorse |
| dprior-method | dprior workhorse |
| dprocess | dprocess workhorse |
| dprocess-method | dprocess workhorse |
| dprocess_spec | dprocess specification |
| eakf | Ensemble Kalman filters |
| eakf-method | 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-method | Effective sample size |
| elementary_algorithms | Elementary computations on POMP models. |
| emeasure | emeasure workhorse |
| emeasure-method | emeasure workhorse |
| emeasure_spec | emeasure specification |
| enkf | Ensemble Kalman filters |
| enkf-method | Ensemble Kalman filters |
| estimation_algorithms | Parameter estimation algorithms for POMP models. |
| euler | rprocess specification |
| eulermultinom | Eulermultinomial and gamma-whitenoise distributions |
| ewcitmeas | Historical childhood disease incidence data |
| ewmeas | Historical childhood disease incidence data |
| expit | Transformations |
| filter_mean | Filtering mean |
| filter_mean-method | Filtering mean |
| filter_traj | Filtering trajectories |
| filter_traj-method | Filtering trajectories |
| flow | flow workhorse |
| flow-method | flow workhorse |
| forecast | Forecast mean |
| forecast-method | Forecast mean |
| freeze | Tools for reproducible computations |
| gillespie | rprocess specification |
| gillespie_hl | rprocess specification |
| 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 |
| kalmanFilter | Kalman filter |
| logit | Transformations |
| logLik | Log likelihood |
| logLik-method | Log likelihood |
| logmeanexp | The log-mean-exp trick |
| log_barycentric | Transformations |
| LondonYorke | Historical childhood disease incidence data |
| lookup | Lookup table |
| map | skeleton specification |
| mcap | Monte Carlo adjusted profile |
| melt | Melt |
| melt-method | Melt |
| mif2 | Iterated filtering: maximum likelihood by iterated, perturbed Bayes maps |
| mif2-method | 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-method | Nonlinear forecasting |
| obs | obs |
| obs-method | obs |
| onestep | rprocess specification |
| ou2 | Two-dimensional discrete-time Ornstein-Uhlenbeck process |
| parameter_trans | parameter transformations |
| parameter_trans-method | parameter transformations |
| parmat | Create a matrix of parameters |
| parmat-method | Create a matrix of parameters |
| partrans | partrans workhorse |
| partrans-method | partrans workhorse |
| parus | Parus major population dynamics |
| periodic_bspline_basis | B-spline bases |
| pfilter | Particle filter |
| pfilter-method | Particle filter |
| plot | pomp plotting facilities |
| plot-method | pomp plotting facilities |
| pmcmc | The particle Markov chain Metropolis-Hastings algorithm |
| pmcmc-method | The particle Markov chain Metropolis-Hastings algorithm |
| pomp | Constructor of the basic pomp object |
| pomp,package | Inference for partially observed Markov processes |
| pomp_constructor | Constructor of the basic pomp object |
| pomp_examples | pre-built pomp examples |
| pred_mean | Prediction mean |
| pred_mean-method | Prediction mean |
| pred_var | Prediction variance |
| pred_var-method | Prediction variance |
| priors | prior specification |
| prior_spec | prior specification |
| probe | Probes (AKA summary statistics) |
| probe-method | 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_objfun | Probe matching |
| probe_objfun-method | Probe matching |
| 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 |
| profile_design | Design matrices for pomp calculations |
| proposals | MCMC proposal distributions |
| rbetabinom | Beta-binomial distribution |
| reproducibility_tools | Tools for reproducible computations |
| reulermultinom | Eulermultinomial and gamma-whitenoise distributions |
| rgammawn | Eulermultinomial and gamma-whitenoise distributions |
| ricker | Ricker model with Poisson observations. |
| rinit | rinit workhorse |
| rinit-method | rinit workhorse |
| rinit_spec | rinit specification |
| rmeasure | rmeasure workhorse |
| rmeasure-method | rmeasure workhorse |
| rmeasure_spec | rmeasure specification |
| rprior | rprior workhorse |
| rprior-method | rprior workhorse |
| rprocess | rprocess workhorse |
| rprocess-method | rprocess workhorse |
| rprocess_spec | rprocess specification |
| runif_design | Design matrices for pomp calculations |
| rw2 | Two-dimensional random-walk process |
| rw_sd | rw_sd |
| sannbox | Simulated annealing with box constraints. |
| saved_states | Saved states |
| saved_states-method | Saved states |
| simulate | Simulations of a partially-observed Markov process |
| simulate-method | Simulations of a partially-observed Markov process |
| sir | Compartmental epidemiological models |
| sir2 | Compartmental epidemiological models |
| SIR_models | Compartmental epidemiological models |
| skeleton | skeleton workhorse |
| skeleton-method | skeleton workhorse |
| skeleton_spec | skeleton specification |
| slice_design | Design matrices for pomp calculations |
| sobol_design | Design matrices for pomp calculations |
| spect | Power spectrum |
| spect-method | Power spectrum |
| spect_match | Spectrum matching |
| spect_objfun | Spectrum matching |
| spect_objfun-method | Spectrum matching |
| spy | Spy |
| spy-method | Spy |
| states | Latent states |
| states-method | Latent states |
| stew | Tools for reproducible computations |
| summary | Summary methods |
| summary-method | Summary methods |
| time | Methods to extract and manipulate the obseration times |
| time-method | Methods to extract and manipulate the obseration times |
| time<- | Methods to extract and manipulate the obseration times |
| time<--method | Methods to extract and manipulate the obseration times |
| timezero | The zero time |
| timezero-method | The zero time |
| timezero<- | The zero time |
| timezero<--method | The zero time |
| traces | Traces |
| traces-method | Traces |
| trajectory | Trajectory of a deterministic model |
| trajectory-method | Trajectory of a deterministic model |
| traj_match | Trajectory matching |
| traj_objfun | Trajectory matching |
| traj_objfun-method | Trajectory matching |
| transformations | Transformations |
| userdata | Facilities for making additional information to basic components |
| vectorfield | skeleton specification |
| verhulst | Verhulst-Pearl model |
| vmeasure | vmeasure workhorse |
| vmeasure-method | vmeasure workhorse |
| vmeasure_spec | vmeasure specification |
| window | Window |
| window-method | Window |
| workhorses | Workhorse functions for the 'pomp' algorithms. |
| wpfilter | Weighted particle filter |
| wpfilter-method | Weighted particle filter |
| wquant | Weighted quantile function |