{ "_id": "6a1018e7acfb0bcc41c85c24", "Package": "pomp", "Type": "Package", "Title": "Statistical Inference for Partially Observed Markov Processes", "Version": "6.4.0.0", "Date": "2025-11-25", "Authors@R": "c(person(given=c(\"Aaron\",\"A.\"),family=\"King\",role=c(\"aut\",\"cre\"),email=\"kingaa@umich.edu\",comment=c(ORCID=\"0000-0001-6159-3207\")),\nperson(given=c(\"Edward\",\"L.\"),family=\"Ionides\",role=\"aut\",comment=c(ORCID=\"0000-0002-4190-0174\"))\t,\nperson(given=\"Carles\",family=\"Bretó\",role=\"aut\",comment=c(ORCID=\"0000-0003-4695-4902\")),\nperson(given=c(\"Stephen\",\"P.\"),family=\"Ellner\",role=\"ctb\",comment=c(ORCID=\"0000-0002-8351-9734\")),\nperson(given=c(\"Matthew\",\"J.\"),family=\"Ferrari\",role=\"ctb\",comment=c(ORCID=\"0000-0001-5251-8168\")),\nperson(given=\"Sebastian\",family=\"Funk\",role=\"ctb\",comment=c(ORCID=\"0000-0002-2842-3406\")),\nperson(given=c(\"Steven\",\"G.\"),family=\"Johnson\",role=\"ctb\"),\nperson(given=c(\"Bruce\",\"E.\"),family=\"Kendall\",role=\"ctb\",comment=c(ORCID=\"0000-0003-1782-8106\")),\nperson(given=c(\"Michael\"),family=\"Lavine\",role=\"ctb\"),\nperson(given=\"Dao\",family=\"Nguyen\",role=\"ctb\",comment=c(ORCID=\"0000-0003-2215-613X\")),\nperson(given=c(\"Eamon\",\"B.\"),family=\"O'Dea\",role=\"ctb\",comment=c(ORCID=\"0000-0003-4748-683X\")),\nperson(given=c(\"Daniel\",\"C.\"),family=\"Reuman\",role=\"ctb\"),\nperson(given=c(\"Helen\"),family=\"Wearing\",role=\"ctb\",comment=c(ORCID=\"0000-0002-9837-9797\")),\nperson(given=c(\"Simon\",\"N.\"),family=\"Wood\",role=\"ctb\",comment=c(ORCID=\"0000-0002-2034-7453\"))\n)", "URL": "https://kingaa.github.io/pomp/", "Description": "Tools for data analysis with partially observed Markov\nprocess (POMP) models (also known as stochastic dynamical\nsystems, hidden Markov models, and nonlinear, non-Gaussian,\nstate-space models). 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"Covariates", "concept": [ "covariates", "implementation information", "interpolation" ], "topics": [ "covariates", "covariate_table", "covariate_table,ANY-method", "covariate_table,character-method", "covariate_table,missing-method", "covariate_table,numeric-method", "repair_lookup_table" ] }, { "page": "covmat", "title": "Estimate a covariance matrix from algorithm traces", "concept": [ "extraction methods" ], "topics": [ "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" ] }, { "page": "csnippet", "title": "C snippets", "concept": [ "implementation information" ], "topics": [ "Csnippet" ] }, { "page": "dacca", "title": "Model of cholera transmission for historic Bengal.", "concept": [ "pomp datasets", "pomp examples" ], "topics": [ "dacca" ] }, { "page": "design", "title": "Design matrices for pomp calculations", "concept": [ "profile 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"reulermultinom", "rgammawn" ] }, { "page": "filter_mean", "title": "Filtering mean", "concept": [ "extraction methods", "particle filter methods" ], "topics": [ "filter_mean", "filter_mean,ANY-method", "filter_mean,kalmand_pomp-method", "filter_mean,missing-method", "filter_mean,pfilterd_pomp-method" ] }, { "page": "filter_traj", "title": "Filtering trajectories", "concept": [ "extraction methods", "particle filter methods" ], "topics": [ "filter_traj", "filter_traj,ANY-method", "filter_traj,listie-method", "filter_traj,missing-method", "filter_traj,pfilterd_pomp-method", "filter_traj,pmcmcd_pomp-method" ] }, { "page": "flow", "title": "flow workhorse", "concept": [ "deterministic methods", "extending the pomp package", "low-level interface", "pomp workhorses" ], "topics": [ "flow", "flow,ANY-method", "flow,missing-method", "flow,pomp-method" ] }, { "page": "forecast", "title": "Forecast mean", "concept": [ "extraction methods" ], "topics": [ "forecast", "forecast,ANY-method", "forecast,kalmand_pomp-method", "forecast,missing-method", "forecast,pfilterd_pomp-method" ] }, { "page": "gompertz", "title": "Gompertz model with log-normal observations.", "concept": [ "pomp examples" ], "topics": [ "gompertz" ] }, { "page": "hitch", "title": "Hitching C snippets and R functions to pomp_fun objects", "concept": [ "extending the pomp package", "low-level interface" ], "topics": [ "hitch" ] }, { "page": "kalman", "title": "Ensemble Kalman filters", "concept": [ "Kalman filter", "elementary algorithms", "particle filter methods" ], "topics": [ "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" ] }, { "page": "kf", "title": "Kalman filter", "concept": [ "Kalman filter" ], "topics": [ "kalmanFilter" ] }, { "page": "loglik", "title": "Log likelihood", "concept": [ "extraction methods" ], "topics": [ "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" ] }, { "page": "logmeanexp", "title": "The log-mean-exp trick", "topics": [ "logmeanexp" ] }, { "page": "lookup", "title": "Lookup table", "concept": [ "interpolation" ], "topics": [ "lookup" ] }, { "page": "mcap", "title": "Monte Carlo adjusted profile", "concept": [ "profile likelihood" ], "topics": [ "mcap" ] }, { "page": "melt", "title": "Melt", "topics": [ "melt", "melt,ANY-method", "melt,array-method", "melt,list-method", "melt,missing-method" ] }, { "page": "mif2", "title": "Iterated filtering: maximum likelihood by iterated, perturbed Bayes maps", "concept": [ "estimation methods", "full-information methods", "methods based on maximization", "particle filter methods" ], "topics": [ "mif2", "mif2,ANY-method", "mif2,data.frame-method", "mif2,mif2d_pomp-method", "mif2,missing-method", "mif2,pfilterd_pomp-method", "mif2,pomp-method" ] }, { "page": "nlf", "title": "Nonlinear forecasting", "concept": [ "estimation methods", "methods based on maximization", "nonlinear forecasting", "summary statistic-based methods" ], "topics": [ "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" ] }, { "page": "obs", "title": "obs", "concept": [ "extraction methods" ], "topics": [ "obs", "obs,ANY-method", "obs,listie-method", "obs,missing-method", "obs,pomp-method" ] }, { "page": "ou2", "title": "Two-dimensional discrete-time Ornstein-Uhlenbeck process", "concept": [ "pomp examples" ], "topics": [ "ou2" ] }, { "page": "parameter_trans", "title": "parameter transformations", "concept": [ "implementation information" ], "topics": [ "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" ] }, { "page": "parmat", "title": "Create a matrix of parameters", "topics": [ "parmat", "parmat,ANY-method", "parmat,array-method", "parmat,data.frame-method", "parmat,missing-method", "parmat,numeric-method" ] }, { "page": "partrans", "title": "partrans workhorse", "concept": [ "extending the pomp package", "low-level interface", "pomp workhorses" ], "topics": [ "partrans", "partrans,ANY-method", "partrans,missing-method", "partrans,objfun-method", "partrans,pomp-method" ] }, { "page": "parus", "title": "Parus major population dynamics", "concept": [ "pomp datasets" ], "topics": [ "parus" ] }, { "page": "pfilter", "title": "Particle filter", "concept": [ "elementary algorithms", "full-information methods", "particle filter methods" ], "topics": [ "pfilter", "pfilter,ANY-method", "pfilter,data.frame-method", "pfilter,missing-method", "pfilter,objfun-method", "pfilter,pfilterd_pomp-method", "pfilter,pomp-method" ] }, { "page": "plot", "title": "pomp plotting facilities", "topics": [ "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" ] }, { "page": "pmcmc", "title": "The particle Markov chain Metropolis-Hastings algorithm", "concept": [ "Bayesian methods", "MCMC methods", "estimation methods", "full-information methods", "particle filter methods" ], "topics": [ "pmcmc", "pmcmc,ANY-method", "pmcmc,data.frame-method", "pmcmc,missing-method", "pmcmc,pfilterd_pomp-method", "pmcmc,pmcmcd_pomp-method", "pmcmc,pomp-method" ] }, { "page": "pomp", "title": "Constructor of the basic pomp object", "concept": [ "implementation information" ], "topics": [ "pomp", "pomp_constructor" ] }, { "page": "pomp_examp", "title": "pre-built pomp examples", "concept": [ "pomp examples" ], "topics": [ "pomp_examples" ] }, { "page": "pred_mean", "title": "Prediction mean", "concept": [ "extraction methods", "particle filter methods" ], "topics": [ "pred_mean", "pred_mean,ANY-method", "pred_mean,kalmand_pomp-method", "pred_mean,missing-method", "pred_mean,pfilterd_pomp-method" ] }, { "page": "pred_var", "title": "Prediction variance", "concept": [ "extraction methods", "particle filter methods" ], "topics": [ "pred_var", "pred_var,ANY-method", "pred_var,missing-method", "pred_var,pfilterd_pomp-method" ] }, { "page": "prior_spec", "title": "prior specification", "concept": [ "Bayesian methods", "implementation information" ], "topics": [ "priors", "prior_spec" ] }, { "page": "probe", "title": "Probes (AKA summary statistics)", "concept": [ "elementary algorithms", "summary statistic-based methods", "synthetic likelihood" ], "topics": [ "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" ] }, { "page": "probe_match", "title": "Probe matching", "concept": [ "estimation methods", "methods based on maximization", "probe matching", "summary statistic-based methods", "synthetic likelihood" ], "topics": [ "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" ] }, { "page": "proposals", "title": "MCMC proposal distributions", "concept": [ "MCMC methods" ], "topics": [ "mvn_diag_rw", "mvn_rw", "mvn_rw_adaptive", "proposals" ] }, { "page": "bake", "title": "Tools for reproducible computations", "concept": [ "reproducibility" ], "topics": [ "append_data", "bake", "freeze", "reproducibility_tools", "stew" ] }, { "page": "ricker", "title": "Ricker model with Poisson observations.", "concept": [ "pomp examples" ], "topics": [ "ricker" ] }, { "page": "rinit", "title": "rinit workhorse", "concept": [ "extending the pomp package", "low-level interface", "pomp workhorses" ], "topics": [ "rinit", "rinit,ANY-method", "rinit,missing-method", "rinit,pomp-method" ] }, { "page": "rinit_spec", "title": "rinit specification", "concept": [ "implementation information" ], "topics": [ "rinit_spec" ] }, { "page": "rmeasure", "title": "rmeasure workhorse", "concept": [ "extending the pomp package", "low-level interface", "pomp workhorses" ], "topics": [ "rmeasure", "rmeasure,ANY-method", "rmeasure,missing-method", "rmeasure,pomp-method" ] }, { "page": "rmeasure_spec", "title": "rmeasure specification", "concept": [ "implementation information" ], "topics": [ "rmeasure_spec" ] }, { "page": "rprior", "title": "rprior workhorse", "concept": [ "Bayesian methods", "extending the pomp package", "low-level interface", "pomp workhorses" ], "topics": [ "rprior", "rprior,ANY-method", "rprior,missing-method", "rprior,pomp-method" ] }, { "page": "rprocess", "title": "rprocess workhorse", "concept": [ "extending the pomp package", "low-level interface", "pomp workhorses" ], "topics": [ "rprocess", "rprocess,ANY-method", "rprocess,missing-method", "rprocess,pomp-method" ] }, { "page": "rprocess_spec", "title": "rprocess specification", "concept": [ "implementation information" ], "topics": [ "discrete_time", "euler", "gillespie", "gillespie_hl", "onestep", "rprocess_spec" ] }, { "page": "rw_sd", "title": "rw_sd", "topics": [ "rw_sd" ] }, { "page": "rw2", "title": "Two-dimensional random-walk process", "concept": [ "pomp examples" ], "topics": [ "rw2" ] }, { "page": "sannbox", "title": "Simulated annealing with box constraints.", "topics": [ "sannbox" ] }, { "page": "saved_states", "title": "Saved states", "concept": [ "extraction methods", "particle filter methods" ], "topics": [ "saved_states", "saved_states,ANY-method", "saved_states,missing-method", "saved_states,pfilterd_pomp-method", "saved_states,pfilterList-method" ] }, { "page": "simulate", "title": "Simulations of a partially-observed Markov process", "concept": [ "elementary algorithms" ], "topics": [ "simulate", "simulate,data.frame-method", "simulate,missing-method", "simulate,objfun-method", "simulate,pomp-method" ] }, { "page": "skeleton", "title": "skeleton workhorse", "concept": [ "deterministic methods", "extending the pomp package", "low-level interface", "pomp workhorses" ], "topics": [ "skeleton", "skeleton,ANY-method", "skeleton,missing-method", "skeleton,pomp-method" ] }, { "page": "skeleton_spec", "title": "skeleton specification", "concept": [ "deterministic methods", "implementation information" ], "topics": [ "map", "skeleton_spec", "vectorfield" ] }, { "page": "spect", "title": "Power spectrum", "concept": [ "elementary algorithms", "summary statistic-based methods" ], "topics": [ "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" ] }, { "page": "spect_match", "title": "Spectrum matching", "concept": [ "estimation methods", "methods based on maximization", "power-spectrum matching", "summary statistic-based methods" ], "topics": [ "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" ] }, { "page": "spy", "title": "Spy", "concept": [ "extraction methods" ], "topics": [ "spy", "spy,ANY-method", "spy,missing-method", "spy,pomp-method" ] }, { "page": "states", "title": "Latent states", "concept": [ "extraction methods" ], "topics": [ "states", "states,ANY-method", "states,listie-method", "states,missing-method", "states,pomp-method" ] }, { "page": "summary", "title": "Summary methods", "concept": [ "extraction methods" ], "topics": [ "summary", "summary,objfun-method", "summary,probed_pomp-method", "summary,spectd_pomp-method" ] }, { "page": "time", "title": "Methods to extract and manipulate the obseration times", "concept": [ "extraction methods" ], "topics": [ "time", "time,listie-method", "time,missing-method", "time,pomp-method", "time<-", "time<-,pomp-method" ] }, { "page": "timezero", "title": "The zero time", "concept": [ "extraction methods" ], "topics": [ "timezero", "timezero,ANY-method", "timezero,missing-method", "timezero,pomp-method", "timezero<-", "timezero<-,ANY-method", "timezero<-,missing-method", "timezero<-,pomp-method" ] }, { "page": "traces", "title": "Traces", "concept": [ "extraction methods" ], "topics": [ "traces", "traces,abcd_pomp-method", 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