In sujit-sahu/bmstdr: Bayesian Modeling of Spatio-Temporal Data with R

bmstdr: Bayesian Modeling of Spatio-Temporal Data with R

Author: Sujit K. Sahu

Date: `r today <- Sys.Date(); format(today, format="%B %d, %Y")`

Introduction:

This is the github page for the R package r BiocStyle::CRANpkg("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 r BiocStyle::CRANpkg("spBayes"), r BiocStyle::CRANpkg("spTimer"), r BiocStyle::CRANpkg("spTDyn"), r BiocStyle::CRANpkg("CARBayes"), r BiocStyle::CRANpkg("CARBayesST") and also code written using computing platforms r BiocStyle::Rpackage("INLA") and r BiocStyle::CRANpkg("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 r BiocStyle::CRANpkg("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.