Man pages for pomp
Statistical Inference for Partially Observed Markov Processes

abcApproximate Bayesian computation
accumvarsaccumulator variables
as_data_frameCoerce to data frame
as_pompas.pomp
bakeTools for reproducible computations
basic_componentsBasic POMP model components.
basic_probesUseful probes for partially-observed Markov processes
betabinomBeta-binomial distribution
blowfliesNicholson's blowflies.
bsfluInfluenza outbreak in a boarding school
bsmc2The Liu and West Bayesian particle filter
bsplinesB-spline bases
childhoodHistorical childhood disease incidence data
coefExtract, set, or alter coefficients
concConcatenate
concatConcatenate
cond_logLikConditional log likelihood
continueContinue an iterative calculation
covariate_tableCovariates
covmatEstimate a covariance matrix from algorithm traces
csnippetC snippets
daccaModel of cholera transmission for historic Bengal.
designDesign matrices for pomp calculations
dinitdinit workhorse
dinit_specdinit specification
dmeasuredmeasure workhorse
dmeasure_specdmeasure specification
dpriordprior workhorse
dprocessdprocess workhorse
dprocess_specdprocess specification
ebolaEbola outbreak, West Africa, 2014-2016
eff_sample_sizeEffective sample size
elementary_algorithmsElementary computations on POMP models.
emeasureemeasure workhorse
emeasure_specemeasure specification
estimation_algorithmsParameter estimation algorithms for POMP models.
eulermultinomEulermultinomial and gamma-whitenoise distributions
filter_meanFiltering mean
filter_trajFiltering trajectories
flowflow workhorse
forecastForecast mean
gompertzGompertz model with log-normal observations.
hitchHitching C snippets and R functions to pomp_fun objects
kalmanEnsemble Kalman filters
kfKalman filter
listielistie
loadLoading and unloading shared-object libraries
loglikLog likelihood
logmeanexpThe log-mean-exp trick
lookupLookup table
mcapMonte Carlo adjusted profile
meltMelt
mif2Iterated filtering: maximum likelihood by iterated, perturbed...
nlfNonlinear forecasting
objfunObjective functions
obsobs
ou2Two-dimensional discrete-time Ornstein-Uhlenbeck process
parameter_transparameter transformations
parmatCreate a matrix of parameters
partranspartrans workhorse
parusParus major population dynamics
pfilterParticle filter
plotpomp plotting facilities
pmcmcThe particle Markov chain Metropolis-Hastings algorithm
pompConstructor of the basic pomp object
pomp_classThe basic pomp class
pomp_examppre-built pomp examples
pomp_funThe "pomp_fun" class
pomp-packageInference for partially observed Markov processes
pred_meanPrediction mean
pred_varPrediction variance
printPrint methods
prior_specprior specification
probeProbes (AKA summary statistics)
probe_matchProbe matching
proposalsMCMC proposal distributions
pStoppStop, pWarn, pMess
resampleResample
rickerRicker model with Poisson observations.
rinitrinit workhorse
rinit_specrinit specification
rmeasurermeasure workhorse
rmeasure_specrmeasure specification
rpriorrprior workhorse
rprocessrprocess workhorse
rprocess_specrprocess specification
rw2Two-dimensional random-walk process
rw_sdrw_sd
sannboxSimulated annealing with box constraints.
saved_statesSaved states
showShow methods
simulateSimulations of a partially-observed Markov process
sirCompartmental epidemiological models
skeletonskeleton workhorse
skeleton_specskeleton specification
spectPower spectrum
spect_matchSpectrum matching
spySpy
statesLatent states
summarySummary methods
timeMethods to extract and manipulate the obseration times
timezeroThe zero time
tracesTraces
trajectoryTrajectory of a deterministic model
traj_matchTrajectory matching
transformationsTransformations
undefinedUndefined
userdataFacilities for making additional information to basic...
verhulstVerhulst-Pearl model
vmeasurevmeasure workhorse
vmeasure_specvmeasure specification
windowWindow
workhorsesWorkhorse functions for the 'pomp' algorithms.
wpfilterWeighted particle filter
wquantWeighted quantile function
pomp documentation built on July 1, 2024, 5:06 p.m.