Canonicalize link into bug tracking systemv0.2.1

Static documentation rebuilt by pkgdown::build_site()

2 files changed, 4 insertions, 3 deletions

diff --git a/docs/articles/chemCal.html b/docs/articles/chemCal.html
index 29db7c8..3da10fd 100644
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@@ -80,7 +80,7 @@
 <a href="#basic-calibration-functions" class="anchor"></a>Basic calibration functions</h1>
 <p>The <code>chemCal</code> package was first designed in the course of a lecture and lab course on “Analytics of Organic Trace Contaminants” at the University of Bremen from October to December 2004. In the fall 2005, an email exchange with Ron Wehrens led to the belief that it would be desirable to implement the inverse prediction method given in <span class="citation">Massart et al. (1997)</span> since it also covers the case of weighted regression. Studies of the IUPAC orange book and of DIN 32645 (equivalent to ISO 11843), publications by <span class="citation">Currie (1997)</span> and the Analytical Method Committee of the Royal Society of Chemistry <span class="citation">(Analytical Methods Committee 1989)</span> and a nice paper by Castells and Castillo <span class="citation">(Castells and Castillo 2000)</span> provided some further understanding of the matter.</p>
 <p>At the moment, the package consists of four functions (<a href="https://pkgdown.jrwb.de/chemCal/reference/calplot.lm.html">calplot</a>, <a href="https://pkgdown.jrwb.de/chemCal/reference/lod.html">lod</a>, <a href="https://pkgdown.jrwb.de/chemCal/reference/loq.html">loq</a> and <a href="https://pkgdown.jrwb.de/chemCal/reference/inverse.predict.html">inverse.predict</a>), working on univariate linear models of class <code>lm</code> or <code>rlm</code>, plus several datasets for validation.</p>
-<p>A <a href="http://bugs.r-project.org/bugzilla3/show_bug.cgi?id=8877">bug report</a> and the following e-mail exchange on the r-devel mailing list about prediction intervals from weighted regression entailed some further studies on this subject. However, I did not encounter any proof or explanation of the formula cited below yet, so I can’t really confirm that Massart’s method is correct.</p>
+<p>A <a href="https://bugs.r-project.org/bugzilla/show_bug.cgi?id=8877">bug report</a> and the following e-mail exchange on the r-devel mailing list about prediction intervals from weighted regression entailed some further studies on this subject. However, I did not encounter any proof or explanation of the formula cited below yet, so I can’t really confirm that Massart’s method is correct.</p>
 <p>In fact, in June 2018 I was made aware of the fact that the inverse prediction method implemented in chemCal version 0.1.37 and before did not take the variance of replicate calibration standards about their means into account, nor the number of replicates when calculating the degrees of freedom. Thanks to PhD student Anna Burniol Figols for reporting this issue!</p>
 <p>As a consequence, I rewrote <code>inverse.predict</code> not to automatically work with the mean responses for each calibration standard any more. The example calculations from <span class="citation">Massart et al. (1997)</span> can still be reproduced when the regression model is calculated using the means of the calibration data as shown below.</p>
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diff --git a/docs/news/index.html b/docs/news/index.html
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@@ -104,11 +104,12 @@
 <a href="#chemcal-0-2-1-2018-07-17" class="anchor"></a>chemCal 0.2.1 (2018-07-17)<small> Unreleased </small>
 </h1>
 <ul>
-<li><p>‘inverse.predict’: Do not work on the means of the calibration standards any more, as this ignores the variability of y values about the means</p></li>
-<li><p>Use testthat for tests to simplify further development. Adapt the tests using data with replicate standard measurements to work on the means in order to show the relation to ‘inverse.predict’ from earlier versions. Include comparisons with investr::calibrate(method = ‘Wald’) for unweighted regressions. Include tests with more precision to check for changes in numerical output across versions.</p></li>
+<li><p>‘inverse.predict’: Do not work on the means of the calibration standards any more, as this ignores the variability of y values about the means. Thanks to Anna Burniol Figols for pointing out this issue</p></li>
+<li><p>Use testthat for tests to simplify development. Adapt the tests using data with replicate standard measurements to work on the means in order to show the relation to ‘inverse.predict’ from earlier versions. Include comparisons with investr::calibrate(method = ‘Wald’) for unweighted regressions. Include tests with more precision to check for changes in numerical output across versions.</p></li>
 <li><p>‘lod’ and ‘loq’: In the lists that are returned, return the list component ‘y’ without names, because we always only have a single element in ‘y’ (previously the name ‘1’ was returned).</p></li>
 <li><p>Convert vignette to html and explain the changes to ‘inverse.predict’</p></li>
 <li><p>Add two example dataset, one from an online course at the University of Toronto, one from Rocke and Lorenzato (1995)</p></li>
+<li><p>Update static documentation</p></li>
 </ul>
 </div>
     <div id="chemcal-0-1-33-2014-04-24" class="section level1">