Using R for Bayesian spatial and spatio-temporal health modeling / Andrew B. Lawson.
Material type:
- still image
- text
- computer
- online resource
- 9781000376722 (ePub ebook)
- 1000376729 (ePub ebook)
- 9781000376708 (PDF ebook)
- 1000376702 (PDF ebook)
- 9781003043997 (ebook)
- 1003043992 (ebook)
- Medical statistics -- Data processing
- Medical mapping -- Data processing
- Medical statistics -- Computer programs
- Geospatial data -- Computer processing
- Geographic information systems
- Information modeling -- Simulation methods
- R (Computer program language)
- Bayesian statistical decision theory
- MATHEMATICS / Probability & Statistics / General
- MEDICAL / Epidemiology
- 610.21 23
- RA409.5
<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"--
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