Inference for Spatiotemporal Partially Observed Markov Processes
An S4 class to represent a spatiotemporal POMP model and data.
Adapted Bagged Filter (ABF)
Adapted Bagged Filter with Intermediate Resampling (ABF-IR)
Calculated log-ARMA log-likelihood benchmark for spatPomp models
Coerce to data frame
Exact log-likelihood for Brownian motion spatPomp generator
Brownian motion spatPomp simulator
Exact log-likelihood for Brownian motion spatPomp generator with share...
Brownian motion spatPomp generator with shared or unit-specific parame...
Block particle filter (BPF)
Concatenate
Concatenate
dunit_measure dunit_measure evaluates the unit measurement density o...
Generalized Ensemble Kalman filter (EnKF)
eunit_measure
Geometric Brownian motion spatPomp simulator
Guided intermediate resampling filter (GIRF)
Measles in UK: spatPomp generator with shared or unit-specific paramet...
Iterated block particle filter (IBPF)
Iterated ensemble Kalman filter (IEnKF)
Iterated guided intermediate resampling filter (IGIRF)
Iterated Unadapted Bagged Filter (IUBF)
listie
Log likelihood
Lorenz '96 spatPomp simulator
Measles in UK spatPomp generator
Measles in UK: spatPomp generator with shared or unit-specific paramet...
munit_measure
Book-keeping functions for working with expanded parameters
Plotting spatPomp data
Print methods
pStop
runit_measure
Simulation of a spatiotemporal partially-observed Markov process
C snippets
Inference for SpatPOMPs (Spatiotemporal Partially Observed Markov Proc...
Constructor of the spatPomp object
Undefined
Unit names of a spatiotemporal model
Vector of measurement densities
Vector of simulated measurements
vunit_measure
Inference on panel data using spatiotemporal partially-observed Markov process (SpatPOMP) models. The 'spatPomp' package extends 'pomp' to include algorithms taking advantage of the spatial structure in order to assist with handling high dimensional processes. See Asfaw et al. (2024) <doi:10.48550/arXiv.2101.01157> for further description of the package.
Useful links