Using R for Bayesian spatial and spatio-temporal health modeling / Andrew B. Lawson.

<P>1. Introduction and Data Sets<BR>2. R Graphics and Spatial Health Data<BR>3. Bayesian Hierarchical Models<BR>4. Computation<BR>5. Bayesian model Goodness of Fit Criteria<BR>6. Bayesian Disease Mapping Models</P><P>Part I Basic Software Approaches</P><P>7. BRugs/OpenBUGS<BR>8. Nimble<BR>9. CARBayes<BR>10. INLA and R-INLA<BR>11. Clustering, Latent Variable and Mixture Modeling<BR>12. Spatio-Temporal Modeling with MCMC<BR>13. Spatio-Temporal Modeling with INLA</P><P>Part II Some Advanced and Special topics</P><P>14. Multivariate Models<BR>15. Survival Modeling<BR>16. Missingness, Measurement Error and Variable Selection<BR>17. Individual Event Modeling<BR>18. Infectious Disease Modeling</P>

"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"--

OCLC-licensed vendor bibliographic record.