Lawson, Andrew B. Using R for Bayesian spatial and spatio-temporal health modeling. (English) Zbl 1467.62005 Chapman & Hall/CRC The R Series. Boca Raton, FL: CRC Press (ISBN 978-0-367-49012-6/hbk; 978-0-367-76067-0/pbk; 978-1-00-304399-7/ebook). xv, 283 p. (2021). Publisher’s description: Progressively more and more attention has been paid to how location affects health outcomes. The area of disease mapping focusses on these problems, and the Bayesian paradigm has a major role to play in the understanding of the complex interplay of context and individual predisposition in such studies of disease. Using R for Bayesian Spatial and Spatio-Temporal Health Modeling provides a major resource for those interested in applying Bayesian methodology in small area health data studies.Features: ● Review of R graphics relevant to spatial health data● Overview of Bayesian methods and Bayesian hierarchical modeling as applied to spatial data● Bayesian Computation and goodness-of-fit● Review of basic Bayesian disease mapping models● Spatio-temporal modeling with MCMC and INLA● Special topics include multivariate models, survival analysis, missing data, measurement error, variable selection, individual event modeling, and infectious disease modeling● Software for fitting models based on BRugs, Nimble, CARBayes and INLA● Provides code relevant to fitting all examples throughout the book at a supplementary website The book fills a void in the literature and available software, providing a crucial link for students and professionals alike to engage in the analysis of spatial and spatio-temporal health data from a Bayesian perspective using R. The book emphasizes the use of MCMC via Nimble, BRugs, and CARBAyes, but also includes INLA for comparative purposes. In addition, a wide range of packages useful in the analysis of geo-referenced spatial data are employed and code is provided. It will likely become a key reference for researchers and students from biostatistics, epidemiology, public health, and environmental science. MSC: 62-02 Research exposition (monographs, survey articles) pertaining to statistics 62P10 Applications of statistics to biology and medical sciences; meta analysis 62H11 Directional data; spatial statistics 62M30 Inference from spatial processes 92-02 Research exposition (monographs, survey articles) pertaining to biology 92C32 Pathology, pathophysiology 92C50 Medical applications (general) 62-08 Computational methods for problems pertaining to statistics Keywords:Bayesian spatial and spatiotemporal health modeling Software:R; INLA; CARBayes; nimble; BRugs PDFBibTeX XMLCite \textit{A. B. Lawson}, Using R for Bayesian spatial and spatio-temporal health modeling. Boca Raton, FL: CRC Press (2021; Zbl 1467.62005) Full Text: DOI