{"product_id":"using-r-for-bayesian-spatial-andrew-b-lawson-9780367490126","title":"Using R for Bayesian Spatial and Spatio-Temporal Health Modeling","description":"\u003cp\u003eProgressively more and more attention has been paid to how location affects health outcomes. The area of \u003ci\u003edisease mapping\u003c\/i\u003e 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. \u003cb\u003e\u003ci\u003eUsing R for Bayesian Spatial and Spatio-Temporal Health Modeling\u003c\/i\u003e\u003c\/b\u003e provides a major resource for those interested in applying Bayesian methodology in small area health data studies.\u003c\/p\u003e \u003cp\u003e\u003c\/p\u003e\u003cb\u003e \u003c\/b\u003e\u003cp\u003eFeatures: \u003c\/p\u003e \u003cp\u003e\u003c\/p\u003e \u003cul\u003e \u003cp\u003e \u003c\/p\u003e\n\u003cli\u003eReview of R graphics relevant to spatial health data\u003c\/li\u003e \u003cp\u003e\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e\n\u003cli\u003eOverview of Bayesian methods and Bayesian hierarchical modeling as applied to spatial data\u003c\/li\u003e \u003cp\u003e\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e\n\u003cli\u003eBayesian Computation and goodness-of-fit\u003c\/li\u003e \u003cp\u003e\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e\n\u003cli\u003eReview of basic Bayesian disease mapping models \u003c\/li\u003e \u003cp\u003e\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e\n\u003cli\u003eSpatio-temporal modeling with MCMC and INLA\u003c\/li\u003e \u003cp\u003e\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e\n\u003cli\u003eSpecial topics include multivariate models, survival analysis, missing data, measurement error, variable selection, individual event modeling, and infectious disease modeling\u003c\/li\u003e \u003cp\u003e\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e\n\u003cli\u003eSoftware for fitting models based on BRugs, Nimble, CARBayes and INLA \u003c\/li\u003e \u003cp\u003e\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e\n\u003cli\u003eProvides code relevant to fitting all examples throughout the book at a supplementary website\u003c\/li\u003e \u003cp\u003e\u003c\/p\u003e\n\u003c\/ul\u003e \u003cp\u003e\u003c\/p\u003e \u003cp\u003eThe 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.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eAuthor:\u003c\/b\u003e Andrew B. Lawson\u003cbr\u003e\u003cb\u003eISBN-10:\u003c\/b\u003e 0367490129\u003cbr\u003e\u003cb\u003eISBN-13:\u003c\/b\u003e 9780367490126\u003cbr\u003e\u003cb\u003ePublisher:\u003c\/b\u003e CRC Press\u003cbr\u003e\u003cb\u003eLanguage:\u003c\/b\u003e English\u003cbr\u003e\u003cb\u003ePublished:\u003c\/b\u003e 04\/29\/2021\u003cbr\u003e\u003cb\u003ePages:\u003c\/b\u003e 284\u003cbr\u003e\u003cb\u003eFormat:\u003c\/b\u003e Hardcover\u003cbr\u003e\u003cb\u003eWeight:\u003c\/b\u003e 1.31lbs\u003cbr\u003e\u003cb\u003eSize:\u003c\/b\u003e 9.21h x 6.14w x 0.69d\u003c\/p\u003e","brand":"Andrew B. Lawson","offers":[{"title":"Hardcover","offer_id":43989193818367,"sku":"9780367490126","price":110.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0662\/2982\/9887\/files\/img_42404c3c-90ca-4bdb-aacb-1e6b8cdd97a1.jpg?v=1683298107","url":"https:\/\/www.whiterainbookhouse.com\/products\/using-r-for-bayesian-spatial-andrew-b-lawson-9780367490126","provider":"WR Book House","version":"1.0","type":"link"}