Package: pomp 5.11.0.3
pomp: Statistical Inference for Partially Observed Markov Processes
Tools for data analysis with partially observed Markov process (POMP) models (also known as stochastic dynamical systems, hidden Markov models, and nonlinear, non-Gaussian, state-space models). The package provides facilities for implementing POMP models, simulating them, and fitting them to time series data by a variety of frequentist and Bayesian methods. It is also a versatile platform for implementation of inference methods for general POMP models.
Authors:
pomp_5.11.0.3.tar.gz
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pomp.pdf |pomp.html✨
pomp/json (API)
NEWS
| # Install 'pomp' in R: |
| install.packages('pomp', repos = c('https://kingaa.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/kingaa/pomp/issues
Datasets:
- LondonYorke - Historical childhood disease incidence data
- blowflies - Nicholson's blowflies.
- bsflu - Influenza outbreak in a boarding school
- ebolaWA2014 - Ebola outbreak, West Africa, 2014-2016
- ewcitmeas - Historical childhood disease incidence data
- ewmeas - Historical childhood disease incidence data
- parus - Parus major population dynamics
abcb-splinedifferential-equationsdynamical-systemsiterated-filteringlikelihoodlikelihood-freemarkov-chain-monte-carlomarkov-modelmathematical-modellingmeasurement-errorparticle-filtersequential-monte-carlosimulation-based-inferencesobol-sequencestate-spacestatistical-inferencestochastic-processestime-series
Last updated 13 days agofrom:5af2a3d03e. Checks:OK: 9. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Nov 08 2024 |
| R-4.5-win-x86_64 | OK | Nov 08 2024 |
| R-4.5-linux-x86_64 | OK | Nov 08 2024 |
| R-4.4-win-x86_64 | OK | Nov 08 2024 |
| R-4.4-mac-x86_64 | OK | Nov 08 2024 |
| R-4.4-mac-aarch64 | OK | Nov 08 2024 |
| R-4.3-win-x86_64 | OK | Nov 08 2024 |
| R-4.3-mac-x86_64 | OK | Nov 08 2024 |
| R-4.3-mac-aarch64 | OK | Nov 08 2024 |
Exports:abcas_pompbakeblowflies1blowflies2bsmc2bspline_basiscoefcoef<-concatcond_logLikcontinuecovariate_tablecovmatCsnippetdaccadbetabinomdeulermultinomdinitdiscrete_timedmeasuredpriordprocesseakfebolaModeleff_sample_sizeemeasureenkfeulerexpitfilter_meanfilter_trajflowforecastfreezegillespiegillespie_hlgompertzhitchinv_log_barycentrickalmanFilterlog_barycentriclogitlogLiklogmeanexplookupmapmcapmeltmif2mvn_diag_rwmvn_rwmvn_rw_adaptivenlf_objfunobsonestepou2parameter_transparmatpartransperiodic_bspline_basispfilterplotpmcmcpomppompLoadpompUnloadpred_meanpred_varprintprobeprobe_acfprobe_ccfprobe_marginalprobe_meanprobe_medianprobe_nlarprobe_objfunprobe_periodprobe_quantileprobe_sdprobe_varprofile_designrbetabinomrepair_lookup_tablereulermultinomrgammawnrickerrinitrmeasurerpriorrprocessrunif_designrw_sdrw2sannboxsaved_statesshowsimulatesirsir2skeletonslice_designsobol_designsolibs<-spectspect_objfunspystatesstewsummarysystematic_resampletimetime<-timezerotimezero<-tracestraj_objfuntrajectoryvectorfieldverhulstvmeasurewindowwpfilterwquant
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Inference for partially observed Markov processes | pomp-package pomp,package |
| Approximate Bayesian computation | abc abc,abcd_pomp-method abc,ANY-method abc,data.frame-method abc,missing-method abc,pomp-method abc,probed_pomp-method |
| accumulator variables | accumvars |
| Basic POMP model components. | basic_components |
| Useful probes for partially-observed Markov processes | basic_probes probe_acf probe_ccf probe_marginal probe_mean probe_median probe_nlar probe_period probe_quantile probe_sd probe_var |
| Beta-binomial distribution | betabinomial dbetabinom rbetabinom |
| Nicholson's blowflies. | blowflies blowflies1 blowflies2 |
| Influenza outbreak in a boarding school | bsflu |
| The Liu and West Bayesian particle filter | bsmc2 bsmc2,ANY-method bsmc2,data.frame-method bsmc2,missing-method bsmc2,pomp-method |
| B-spline bases | bsplines bspline_basis periodic_bspline_basis |
| Historical childhood disease incidence data | childhood_disease_data ewcitmeas ewmeas LondonYorke |
| Extract, set, or alter coefficients | coef coef,listie-method coef,objfun-method coef,pomp-method coef<- coef<-,missing-method coef<-,objfun-method coef<-,pomp-method |
| Compartmental epidemiological models | compartmental_models sir sir2 SIR_models |
| Concatenate | c c.Pomp concat |
| Conditional log likelihood | cond_logLik cond_logLik,ANY-method cond_logLik,bsmcd_pomp-method cond_logLik,kalmand_pomp-method cond_logLik,missing-method cond_logLik,pfilterd_pomp-method cond_logLik,pfilterList-method cond_logLik,wpfilterd_pomp-method |
| Continue an iterative calculation | continue continue,abcd_pomp-method continue,ANY-method continue,mif2d_pomp-method continue,missing-method continue,pmcmcd_pomp-method |
| Covariates | covariates covariate_table covariate_table,ANY-method covariate_table,character-method covariate_table,missing-method covariate_table,numeric-method repair_lookup_table |
| Estimate a covariance matrix from algorithm traces | covmat covmat,abcd_pomp-method covmat,abcList-method covmat,ANY-method covmat,missing-method covmat,pmcmcd_pomp-method covmat,pmcmcList-method covmat,probed_pomp-method |
| C snippets | Csnippet |
| Model of cholera transmission for historic Bengal. | dacca |
| Design matrices for pomp calculations | design profile_design runif_design slice_design sobol_design |
| dinit workhorse | dinit dinit,ANY-method dinit,missing-method dinit,pomp-method |
| dinit specification | dinit_spec |
| dmeasure workhorse | dmeasure dmeasure,ANY-method dmeasure,missing-method dmeasure,pomp-method |
| dmeasure specification | dmeasure_spec |
| dprior workhorse | dprior dprior,ANY-method dprior,missing-method dprior,pomp-method |
| dprocess workhorse | dprocess dprocess,ANY-method dprocess,missing-method dprocess,pomp-method |
| dprocess specification | dprocess_spec |
| Ebola outbreak, West Africa, 2014-2016 | ebola ebolaModel ebolaWA2014 |
| Effective sample size | eff_sample_size eff_sample_size,ANY-method eff_sample_size,bsmcd_pomp-method eff_sample_size,missing-method eff_sample_size,pfilterd_pomp-method eff_sample_size,pfilterList-method eff_sample_size,wpfilterd_pomp-method |
| Elementary computations on POMP models. | elementary_algorithms |
| emeasure workhorse | emeasure emeasure,ANY-method emeasure,missing-method emeasure,pomp-method |
| emeasure specification | emeasure_spec |
| Parameter estimation algorithms for POMP models. | estimation_algorithms |
| Eulermultinomial and gamma-whitenoise distributions | deulermultinom eulermultinom reulermultinom rgammawn |
| Filtering mean | filter_mean filter_mean,ANY-method filter_mean,kalmand_pomp-method filter_mean,missing-method filter_mean,pfilterd_pomp-method |
| Filtering trajectories | filter_traj filter_traj,ANY-method filter_traj,listie-method filter_traj,missing-method filter_traj,pfilterd_pomp-method filter_traj,pmcmcd_pomp-method |
| flow workhorse | flow flow,ANY-method flow,missing-method flow,pomp-method |
| Forecast mean | forecast forecast,ANY-method forecast,kalmand_pomp-method forecast,missing-method forecast,pfilterd_pomp-method |
| Gompertz model with log-normal observations. | gompertz |
| Hitching C snippets and R functions to pomp_fun objects | hitch |
| Ensemble Kalman filters | eakf eakf,ANY-method eakf,data.frame-method eakf,missing-method eakf,pomp-method enkf enkf,ANY-method enkf,data.frame-method enkf,kalmand_pomp-method enkf,missing-method enkf,pomp-method kalman |
| Kalman filter | kalmanFilter |
| Log likelihood | logLik logLik,ANY-method logLik,bsmcd_pomp-method logLik,kalmand_pomp-method logLik,listie-method logLik,missing-method logLik,nlf_objfun-method logLik,objfun-method logLik,pfilterd_pomp-method logLik,pmcmcd_pomp-method logLik,probed_pomp-method logLik,spect_match_objfun-method logLik,wpfilterd_pomp-method |
| The log-mean-exp trick | logmeanexp |
| Lookup table | lookup |
| Monte Carlo adjusted profile | mcap |
| Melt | melt melt,ANY-method melt,array-method melt,list-method melt,missing-method |
| Iterated filtering: maximum likelihood by iterated, perturbed Bayes maps | mif2 mif2,ANY-method mif2,data.frame-method mif2,mif2d_pomp-method mif2,missing-method mif2,pfilterd_pomp-method mif2,pomp-method |
| Nonlinear forecasting | nlf nlf_objfun nlf_objfun,ANY-method nlf_objfun,data.frame-method nlf_objfun,missing-method nlf_objfun,nlf_objfun-method nlf_objfun,pomp-method |
| obs | obs obs,ANY-method obs,listie-method obs,missing-method obs,pomp-method |
| Two-dimensional discrete-time Ornstein-Uhlenbeck process | ou2 |
| parameter transformations | parameter_trans parameter_trans,ANY,ANY-method parameter_trans,ANY,missing-method parameter_trans,character,character-method parameter_trans,Csnippet,Csnippet-method parameter_trans,function,function-method parameter_trans,missing,ANY-method parameter_trans,missing,missing-method parameter_trans,NULL,NULL-method parameter_trans,pomp_fun,pomp_fun-method |
| Create a matrix of parameters | parmat parmat,ANY-method parmat,array-method parmat,data.frame-method parmat,missing-method parmat,numeric-method |
| partrans workhorse | partrans partrans,ANY-method partrans,missing-method partrans,objfun-method partrans,pomp-method |
| Parus major population dynamics | parus |
| Particle filter | pfilter pfilter,ANY-method pfilter,data.frame-method pfilter,missing-method pfilter,objfun-method pfilter,pfilterd_pomp-method pfilter,pomp-method |
| pomp plotting facilities | plot plot,Abc-method plot,bsmcd_pomp-method plot,Mif2-method plot,missing-method plot,Pmcmc-method plot,pomp-method plot,pomp_plottable-method plot,probed_pomp-method plot,probe_match_objfun-method plot,spectd_pomp-method plot,spect_match_objfun-method |
| The particle Markov chain Metropolis-Hastings algorithm | pmcmc pmcmc,ANY-method pmcmc,data.frame-method pmcmc,missing-method pmcmc,pfilterd_pomp-method pmcmc,pmcmcd_pomp-method pmcmc,pomp-method |
| Constructor of the basic pomp object | pomp pomp_constructor |
| pre-built pomp examples | pomp_examples |
| Prediction mean | pred_mean pred_mean,ANY-method pred_mean,kalmand_pomp-method pred_mean,missing-method pred_mean,pfilterd_pomp-method |
| Prediction variance | pred_var pred_var,ANY-method pred_var,missing-method pred_var,pfilterd_pomp-method |
| prior specification | priors prior_spec |
| Probes (AKA summary statistics) | probe probe,ANY-method probe,data.frame-method probe,missing-method probe,objfun-method probe,pomp-method probe,probed_pomp-method probe,probe_match_objfun-method |
| Probe matching | probe_match probe_objfun probe_objfun,ANY-method probe_objfun,data.frame-method probe_objfun,missing-method probe_objfun,pomp-method probe_objfun,probed_pomp-method probe_objfun,probe_match_objfun-method |
| MCMC proposal distributions | mvn_diag_rw mvn_rw mvn_rw_adaptive proposals |
| Tools for reproducible computations | bake freeze reproducibility_tools stew |
| Ricker model with Poisson observations. | ricker |
| rinit workhorse | rinit rinit,ANY-method rinit,missing-method rinit,pomp-method |
| rinit specification | rinit_spec |
| rmeasure workhorse | rmeasure rmeasure,ANY-method rmeasure,missing-method rmeasure,pomp-method |
| rmeasure specification | rmeasure_spec |
| rprior workhorse | rprior rprior,ANY-method rprior,missing-method rprior,pomp-method |
| rprocess workhorse | rprocess rprocess,ANY-method rprocess,missing-method rprocess,pomp-method |
| rprocess specification | discrete_time euler gillespie gillespie_hl onestep rprocess_spec |
| rw_sd | rw_sd |
| Two-dimensional random-walk process | rw2 |
| Simulated annealing with box constraints. | sannbox |
| Saved states | saved_states saved_states,ANY-method saved_states,missing-method saved_states,pfilterd_pomp-method saved_states,pfilterList-method |
| Simulations of a partially-observed Markov process | simulate simulate,data.frame-method simulate,missing-method simulate,objfun-method simulate,pomp-method |
| skeleton workhorse | skeleton skeleton,ANY-method skeleton,missing-method skeleton,pomp-method |
| skeleton specification | map skeleton_spec vectorfield |
| Power spectrum | spect spect,ANY-method spect,data.frame-method spect,missing-method spect,objfun-method spect,pomp-method spect,spectd_pomp-method spect,spect_match_objfun-method |
| Spectrum matching | spect_match spect_objfun spect_objfun,ANY-method spect_objfun,data.frame-method spect_objfun,missing-method spect_objfun,pomp-method spect_objfun,spectd_pomp-method spect_objfun,spect_match_objfun-method |
| Spy | spy spy,ANY-method spy,missing-method spy,pomp-method |
| Latent states | states states,ANY-method states,listie-method states,missing-method states,pomp-method |
| Summary methods | summary summary,objfun-method summary,probed_pomp-method summary,spectd_pomp-method |
| Methods to extract and manipulate the obseration times | time time,listie-method time,missing-method time,pomp-method time<- time<-,pomp-method |
| The zero time | timezero timezero,ANY-method timezero,missing-method timezero,pomp-method timezero<- timezero<-,ANY-method timezero<-,missing-method timezero<-,pomp-method |
| Traces | traces traces,abcd_pomp-method traces,abcList-method traces,ANY-method traces,mif2d_pomp-method traces,mif2List-method traces,missing-method traces,pmcmcd_pomp-method traces,pmcmcList-method |
| Trajectory matching | traj_match traj_objfun traj_objfun,ANY-method traj_objfun,data.frame-method traj_objfun,missing-method traj_objfun,pomp-method traj_objfun,traj_match_objfun-method |
| Trajectory of a deterministic model | trajectory trajectory,ANY-method trajectory,data.frame-method trajectory,missing-method trajectory,pomp-method trajectory,traj_match_objfun-method |
| Transformations | expit inv_log_barycentric logit log_barycentric transformations |
| Facilities for making additional information to basic components | userdata |
| Verhulst-Pearl model | verhulst |
| vmeasure workhorse | vmeasure vmeasure,ANY-method vmeasure,missing-method vmeasure,pomp-method |
| vmeasure specification | vmeasure_spec |
| Window | window window,pomp-method |
| Workhorse functions for the 'pomp' algorithms. | workhorses |
| Weighted particle filter | wpfilter wpfilter,ANY-method wpfilter,data.frame-method wpfilter,missing-method wpfilter,pomp-method wpfilter,wpfilterd_pomp-method |
| Weighted quantile function | wquant |
