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bmstdr: Bayesian Modeling of Spatio-Temporal Data with R

Date: April 07, 2022


This is the github page for the R package bmstdr. This is the companion R package for the book Bayesian Modeling of Spatio-Temporal Data with R published by Chapman and Hall.

The package facilitates Bayesian modeling of both point referenced and areal unit data with or without temporal replications. Three main functions in the package: Bspatial for spatial only point referenced data, Bsptime for spatio-temporal point reference data and Bcartime for areal unit data, which may also vary in time, perform the main modeling and validation tasks. Computations and inference in a Bayesian modeling framework are done using popular R software packages such as spBayes, spTimer, spTDyn, CARBayes, CARBayesST and also code written using computing platforms INLA and rstan.

Point referenced data are modeled using the Gaussian error distribution only but a top level generalized linear model is used for areal data modeling. The user of *[bmstdr](* is afforded the flexibility to choose an appropriate package and is also free to name the rows of their input data frame for validation purposes. The package incorporates a range of prior distributions allowable in the nominated packages with default hyper-parameter values. The package allows quick comparison of models using both model choice criteria, such as DIC and WAIC, and facilitates K-fold cross-validation without much programming effort. Familiar diagnostic plots and model fit exploration using the S3 methods such as `summary`, `residuals` and `plot` are included so that a beginner user confident in model fitting using the base `R` function `lm` can quickly learn to analyzing data by fitting a range of appropriate spatial and spatio-temporal models. The full vignette illustrates the package using five built-in data sets. Three of these are on point referenced data on air pollution and temperature at the deep ocean, and the other two are areal unit data on Covid-19 mortality in England.