Using R for Bayesian Spatial and Spatio-temporal Health Modeling

Using R for Bayesian Spatial and Spatio-temporal Health Modeling by Andrew Lawson, published by CRC Press in 2021, is a comprehensive resource that addresses the analysis of spatial and spatio-temporal health data through a Bayesian lens using R. This 284-page book serves as a crucial link for both students and professionals, offering insights into the application of various methodologies, including MCMC via Nimble, BRugs, and CARBAyes, while also incorporating INLA for comparative analysis.

Readers will find a detailed exploration of the tools and packages essential for analyzing geo-referenced spatial data. The book provides practical code examples, making it a valuable reference for those in fields such as biostatistics, epidemiology, public health, and environmental science. By focusing on the intersection of computers, mathematics, and health sciences, this edition aims to enhance understanding and application of Bayesian methods in health modeling.