# Statistical Inference for Partially Observed Markov Processes

## Documentation for package ‘pomp’ version 4.2

- DESCRIPTION file.
- User guides, package vignettes and other documentation.
- Package NEWS.

## Help Pages

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

## -- A --

abc | Approximate Bayesian computation
abc-method | Approximate Bayesian computation
accumulator variables | accumulator variables
accumvars | accumulator variables
approximate Bayesian computation | Approximate Bayesian computation

## -- B --

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
bspline.basis | B-spline bases
bsplines | B-spline bases

## -- C --

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
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

## -- D --

dacca | Model of cholera transmission for historic Bengal.
dbetabinom | Beta-binomial distribution
design | Design matrices for pomp calculations
deulermultinom | Probability distributions
discrete_time | The latent state process simulator
distributions | Probability distributions
dmeasure | dmeasure
dmeasure specification | The measurement model density
dmeasure-method | dmeasure
dprior | dprior
dprior-method | dprior
dprocess | dprocess
dprocess specification | The latent state process density
dprocess-method | dprocess

## -- E --

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
emeasure specification | The expectation of the measurement model
emeasure-method | emeasure
enkf | Ensemble Kalman filters
enkf-method | Ensemble Kalman filters
estimation algorithms | Parameter estimation algorithms for POMP models.
euler | The latent state process simulator
ewcitmeas | Historical childhood disease incidence data
ewmeas | Historical childhood disease incidence data
expit | Transformations

## -- F --

filter.mean | Filtering mean
filter.mean-method | Filtering mean
filter.traj | Filtering trajectories
filter.traj-method | Filtering trajectories
flow | Flow of a deterministic model
flow-method | Flow of a deterministic model
forecast | Forecast mean
forecast-method | Forecast mean
freeze | Tools for reproducible computations.

## -- G --

gillespie | The latent state process simulator
gillespie_hl | The latent state process simulator
gompertz | Gompertz model with log-normal observations.

## -- H --

hitch | Hitching C snippets and R functions to pomp_fun objects

## -- I --

inv_log_barycentric | Transformations

## -- K --

kalman | Ensemble Kalman filters
kalmanFilter | Kalman filter

## -- L --

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

## -- M --

map | The deterministic skeleton of a model
mcap | Monte Carlo adjusted profile
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

## -- N --

nlf | Nonlinear forecasting
nlf_objfun | Nonlinear forecasting
nlf_objfun-method | Nonlinear forecasting
nonlinear forecasting | Nonlinear forecasting

## -- O --

obs | obs
obs-method | obs
onestep | The latent state process simulator
ou2 | Two-dimensional discrete-time Ornstein-Uhlenbeck process

## -- P --

parameter transformations | Parameter transformations
parameter_trans | Parameter transformations
parameter_trans-method | Parameter transformations
parmat | Create a matrix of parameters
parmat-method | Create a matrix of parameters
partrans | partrans
partrans-method | partrans
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 examples | pomp_examples
pomp,package | Inference for partially observed Markov processes
pred.mean | Prediction mean
pred.mean-method | Prediction mean
pred.var | Prediction variance
pred.var-method | Prediction variance
prior specification | prior distribution
probe | Probes (AKA summary statistics)
probe matching | Probe matching
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.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-method | Probe matching
profile_design | Design matrices for pomp calculations
proposals | MCMC proposal distributions

## -- R --

rbetabinom | Beta-binomial distribution
reproducibility tools | Tools for reproducible computations.
reulermultinom | Probability distributions
rgammawn | Probability distributions
ricker | Ricker model with Poisson observations.
rinit | rinit
rinit specification | The initial-state distribution
rinit-method | rinit
rmeasure | rmeasure
rmeasure specification | The measurement-model simulator
rmeasure-method | rmeasure
rprior | rprior
rprior-method | rprior
rprocess | rprocess
rprocess specification | The latent state process simulator
rprocess-method | rprocess
runif_design | Design matrices for pomp calculations
rw.sd | rw.sd
rw2 | Two-dimensional random-walk process

## -- S --

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
SIR models | Compartmental epidemiological models
sir2 | Compartmental epidemiological models
skeleton | skeleton
skeleton specification | The deterministic skeleton of a model
skeleton-method | skeleton
slice_design | Design matrices for pomp calculations
sobol_design | Design matrices for pomp calculations
spect | Power spectrum
spect-method | Power spectrum
spectrum matching | 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

## -- T --

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 matching | Trajectory matching
trajectory-method | Trajectory of a deterministic model
traj_objfun | Trajectory matching
traj_objfun-method | Trajectory matching
transformations | Transformations

## -- U --

userdata | Facilities for making additional information to basic components

## -- V --

vectorfield | The deterministic skeleton of a model
verhulst | Verhulst-Pearl model
vmeasure | vmeasure
vmeasure specification | The variance of the measurement model
vmeasure-method | vmeasure

## -- W --

window | Window
window-method | Window
workhorses | Workhorse functions for the 'pomp' algorithms.
wpfilter | Weighted particle filter
wpfilter-method | Weighted particle filter