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diff --git a/index.md b/index.md
@@ -8,7 +8,7 @@ documentclass: krantz
 monofont: "Source Code Pro"
 monofontoptions: "Scale=0.7"
 bibliography:
-  - geocompr.bib
+  - geocompr.json
   - packages.bib
 biblio-style: apalike
 link-citations: yes

diff --git a/intro.html b/intro.html
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@@ -360,7 +362,7 @@ <h2>
 
 The 1990s saw the development of numerous S scripts and a handful of packages for spatial statistics.
 R packages arose from these and by 2000 there were R packages for various spatial methods “point pattern analysis, geostatistics, exploratory spatial data analysis and spatial econometrics,” according to an <a href="http://www.geocomputation.org/2000/GC009/Gc009.htm">article</a> presented at GeoComputation 2000 <span class="citation">(<a href="references.html#ref-bivand_open_2000" role="doc-biblioref">R. Bivand and Neteler 2000</a>)</span>.
-Some of these, notably <strong>spatial</strong>, <strong>sgeostat</strong> and <strong>splancs</strong> are still available on CRAN <span class="citation">(<a href="references.html#ref-rowlingson_splancs_1993" role="doc-biblioref">B. S. Rowlingson and Diggle 1993</a>; <a href="references.html#ref-rowlingson_splancs_2017" role="doc-biblioref">B. Rowlingson and Diggle 2017</a>; <a href="references.html#ref-venables_modern_2002" role="doc-biblioref">Venables and Ripley 2002</a>; <a href="references.html#ref-majure_sgeostat_2016" role="doc-biblioref">Majure and Gebhardt 2016</a>)</span>.</p>
+Some of these, notably <strong>spatial</strong>, <strong>sgeostat</strong> and <strong>splancs</strong> are still available on CRAN <span class="citation">(<a href="references.html#ref-rowlingson_splancs_1993" role="doc-biblioref">B. S. Rowlingson and Diggle 1993</a>; <a href="references.html#ref-rowlingson_splancs_2017" role="doc-biblioref">B. Rowlingson and Diggle 2017</a>; <a href="references.html#ref-venables_modern_2002" role="doc-biblioref">W. N. Venables and Ripley 2002</a>; <a href="references.html#ref-majure_sgeostat_2016" role="doc-biblioref">Majure and Gebhardt 2016</a>)</span>.</p>
 <p>A subsequent article in R News (the predecessor of <a href="https://journal.r-project.org/">The R Journal</a>) contained an overview of spatial statistical software in R at the time, much of which was based on previous code written for S/S-PLUS <span class="citation">(<a href="references.html#ref-ripley_spatial_2001" role="doc-biblioref">Ripley 2001</a>)</span>.
 This overview described packages for spatial smoothing and interpolation, including <strong>akima</strong> and <strong>geoR</strong> <span class="citation">(<a href="references.html#ref-akima_akima_2016" role="doc-biblioref">Akima and Gebhardt 2016</a>; <a href="references.html#ref-jr_geor_2016" role="doc-biblioref">Jr and Diggle 2016</a>)</span>, and point pattern analysis, including <strong>splancs</strong> <span class="citation">(<a href="references.html#ref-rowlingson_splancs_2017" role="doc-biblioref">B. Rowlingson and Diggle 2017</a>)</span> and <strong>spatstat</strong> <span class="citation">(<a href="references.html#ref-baddeley_spatial_2015" role="doc-biblioref">Baddeley, Rubak, and Turner 2015</a>)</span>.</p>
 <p>The following R News issue (Volume 1/3) put spatial packages in the spotlight again, with a more detailed introduction to <strong>splancs</strong> and a commentary on future prospects regarding spatial statistics <span class="citation">(<a href="references.html#ref-bivand_more_2001" role="doc-biblioref">R. Bivand 2001</a>)</span>.

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diff --git a/preface.html b/preface.html
@@ -27,6 +27,8 @@
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@@ -129,7 +131,7 @@ <h2>How to read this book<a class="anchor" aria-label="anchor" href="#how-to-rea
 However, we recommend reading about the wider context of <em>Geocomputation with R</em> in Chapter <a href="intro.html#intro">1</a> first.
 If you are new to R, we also recommend learning more about the language before attempting to run the code chunks provided in each chapter (unless you’re reading the book for an understanding of the concepts).
 Fortunately for R beginners R has a supportive community that has developed a wealth of resources that can help.
-We particularly recommend three tutorials: <a href="http://r4ds.had.co.nz/">R for Data Science</a> <span class="citation">(<a href="references.html#ref-grolemund_r_2016" role="doc-biblioref">Grolemund and Wickham 2016</a>)</span> and <a href="https://csgillespie.github.io/efficientR/">Efficient R Programming</a> <span class="citation">(<a href="references.html#ref-gillespie_efficient_2016" role="doc-biblioref">Gillespie and Lovelace 2016</a>)</span>, especially <a href="https://csgillespie.github.io/efficientR/set-up.html#r-version">Chapter 2</a> (on installing and setting-up R/RStudio) and <a href="https://csgillespie.github.io/efficientR/learning.html">Chapter 10</a> (on learning to learn), and <a href="http://colinfay.me/intro-to-r/">An introduction to R</a> <span class="citation">(<a href="references.html#ref-venables_introduction_2017" role="doc-biblioref">Venables, Smith, and Team 2017</a>)</span>.</p>
+We particularly recommend three tutorials: <a href="http://r4ds.had.co.nz/">R for Data Science</a> <span class="citation">(<a href="references.html#ref-grolemund_r_2016" role="doc-biblioref">Grolemund and Wickham 2016</a>)</span> and <a href="https://csgillespie.github.io/efficientR/">Efficient R Programming</a> <span class="citation">(<a href="references.html#ref-gillespie_efficient_2016" role="doc-biblioref">Gillespie and Lovelace 2016</a>)</span>, especially <a href="https://csgillespie.github.io/efficientR/set-up.html#r-version">Chapter 2</a> (on installing and setting-up R/RStudio) and <a href="https://csgillespie.github.io/efficientR/learning.html">Chapter 10</a> (on learning to learn), and <a href="http://colinfay.me/intro-to-r/">An introduction to R</a> <span class="citation">(<a href="references.html#ref-venables_introduction_2017" role="doc-biblioref">W. N. Venables, Smith, and R Core Team 2017</a>)</span>.</p>
 </div>
 <div id="why-r" class="section level2 unnumbered">
 <h2>Why R?<a class="anchor" aria-label="anchor" href="#why-r"><i class="fas fa-link"></i></a>

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