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  <titleInfo>
    <title>Using R for Bayesian spatial and spatio-temporal health modeling</title>
  </titleInfo>
  <name type="personal">
    <namePart>Lawson, Andrew (Andrew B.)</namePart>
    <role>
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    <place>
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    <dateIssued encoding="marc">2021</dateIssued>
    <edition>1st.</edition>
    <issuance>monographic</issuance>
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  <language>
    <languageTerm authority="iso639-2b" type="code">eng</languageTerm>
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  <physicalDescription>
    <extent>1 online resource : illustrations (black and white).</extent>
  </physicalDescription>
  <abstract>"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"--</abstract>
  <note type="statement of responsibility">Andrew B. Lawson.</note>
  <note>&lt;P&gt;1. Introduction and Data Sets&lt;BR&gt;2. R Graphics and Spatial Health Data&lt;BR&gt;3. Bayesian Hierarchical Models&lt;BR&gt;4. Computation&lt;BR&gt;5. Bayesian model Goodness of Fit Criteria&lt;BR&gt;6. Bayesian Disease Mapping Models&lt;/P&gt;&lt;P&gt;Part I Basic Software Approaches&lt;/P&gt;&lt;P&gt;7. BRugs/OpenBUGS&lt;BR&gt;8. Nimble&lt;BR&gt;9. CARBayes&lt;BR&gt;10. INLA and R-INLA&lt;BR&gt;11. Clustering, Latent Variable and Mixture Modeling&lt;BR&gt;12. Spatio-Temporal Modeling with MCMC&lt;BR&gt;13. Spatio-Temporal Modeling with INLA&lt;/P&gt;&lt;P&gt;Part II Some Advanced and Special topics&lt;/P&gt;&lt;P&gt;14. Multivariate Models&lt;BR&gt;15. Survival Modeling&lt;BR&gt;16. Missingness, Measurement Error and Variable Selection&lt;BR&gt;17. Individual Event Modeling&lt;BR&gt;18. Infectious Disease Modeling&lt;/P&gt;</note>
  <subject authority="lcsh">
    <topic>Medical statistics</topic>
    <topic>Data processing</topic>
  </subject>
  <subject authority="lcsh">
    <topic>Medical mapping</topic>
    <topic>Data processing</topic>
  </subject>
  <subject authority="lcsh">
    <topic>Medical statistics</topic>
    <topic>Computer programs</topic>
  </subject>
  <subject authority="lcsh">
    <topic>Geospatial data</topic>
    <topic>Computer processing</topic>
  </subject>
  <subject authority="lcsh">
    <topic>Geographic information systems</topic>
  </subject>
  <subject authority="lcsh">
    <topic>Information modeling</topic>
    <topic>Simulation methods</topic>
  </subject>
  <subject authority="lcsh">
    <topic>R (Computer program language)</topic>
  </subject>
  <subject authority="lcsh">
    <topic>Bayesian statistical decision theory</topic>
  </subject>
  <subject authority="bisacsh">
    <topic>MATHEMATICS / Probability &amp; Statistics / General</topic>
  </subject>
  <subject authority="bisacsh">
    <topic>MEDICAL / Epidemiology</topic>
  </subject>
  <classification authority="lcc">RA409.5</classification>
  <classification authority="ddc" edition="23">610.21</classification>
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    <titleInfo>
      <title>Chapman &amp; Hall/CRC the R series</title>
    </titleInfo>
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  <identifier type="isbn">9781000376722 (ePub ebook)</identifier>
  <identifier type="isbn">1000376729 (ePub ebook)</identifier>
  <identifier type="isbn">9781000376708 (PDF ebook)</identifier>
  <identifier type="isbn">1000376702 (PDF ebook)</identifier>
  <identifier type="isbn">9781003043997 (ebook)</identifier>
  <identifier type="isbn">1003043992 (ebook)</identifier>
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