update

git-svn-id: svn://svn.r-forge.r-project.org/svnroot/ctm@2506 edb9625f-4e0d-4859-8d74-9fd3b1da38cb

Author: thothorn

www/images/BHplot-1.png

@@ -267,6 +267,10 @@ <h1 id="scholarly-papers-describing-the-methodology">Scholarly papers describing
 understood as univariate transformation models and their joint distribution
 is characterised by a (gaussian) copula, are described in <a id="cite-Klein_Hothorn_Barbanti_2020"></a><a href="https://doi.org/10.1111/sjos.12501">Klein, Hothorn, Barbanti, and Kneib (2022)</a>.</p>
 
+<p>Applications of transformation models to diagnostic testing <a id="cite-Sewak_Hothorn_2023"></a>(<a href="https://doi.org/10.1177/09622802231176030">Sewak and Hothorn, 2023</a>), location-scale regression <a id="cite-Siegfried_Kook_Hothorn_2023"></a>(<a href="https://doi.org/10.1080/00031305.2023.2203177">Siegfried, Kook, and Hothorn, 2023</a>)
+or to mixed models <a id="cite-Tamasi_Hothorn_2021"></a><a id="cite-Tamasi_Crowther_Puhan_2022"></a>(<a href="https://doi.org/10.32614/RJ-2021-075">Tamási and Hothorn, 2021</a>; <a href="https://doi.org/10.1093/biostatistics/kxab045">Tamási, Crowther, Puhan, Steyerberg, and Hothorn, 2022</a>) and transformation models for correlated observations
+<a id="cite-Barbanti_Hothorn_2023"></a>(<a href="https://doi.org/10.1093/biostatistics/kxac048">Barbanti and Hothorn, 2024</a>) have been published in addition.</p>
+
 <p><strong>References</strong></p>
 
 <p><a id="bib-Hothorn_Kneib_Buehlmann_2014"></a><a href="#cite-Hothorn_Kneib_Buehlmann_2014">[1]</a><cite>
@@ -318,12 +322,45 @@ <h1 id="scholarly-papers-describing-the-methodology">Scholarly papers describing
 In: <em>Journal of Computational and Graphical Statistics</em> 14 (2021), pp. 144&ndash;148.
 DOI: <a href="https://doi.org/10.1080/10618600.2021.1872581">10.1080/10618600.2021.1872581</a>.</cite></p>
 
-<p><a id="bib-Klein_Hothorn_Barbanti_2020"></a><a href="#cite-Klein_Hothorn_Barbanti_2020">[9]</a><cite>
+<p><a id="bib-Tamasi_Hothorn_2021"></a><a href="#cite-Tamasi_Hothorn_2021">[9]</a><cite>
+B. Tamási and T. Hothorn.
+&ldquo;tramME: Mixed-Effects Transformation Models Using
+Template Model Builder&rdquo;.
+In: <em>The R Journal</em> 13.2 (2021), pp. 398&ndash;418.
+DOI: <a href="https://doi.org/10.32614/RJ-2021-075">10.32614/RJ-2021-075</a>.</cite></p>
+
+<p><a id="bib-Klein_Hothorn_Barbanti_2020"></a><a href="#cite-Klein_Hothorn_Barbanti_2020">[10]</a><cite>
 N. Klein, T. Hothorn, L. Barbanti, and T. Kneib.
 &ldquo;Multivariate Conditional Transformation Models&rdquo;.
 In: <em>Scandinavian Journal of Statistics</em> 49 (2022), pp. 116&ndash;142.
 DOI: <a href="https://doi.org/10.1111/sjos.12501">10.1111/sjos.12501</a>.</cite></p>
 
+<p><a id="bib-Tamasi_Crowther_Puhan_2022"></a><a href="#cite-Tamasi_Crowther_Puhan_2022">[11]</a><cite>
+B. Tamási, M. Crowther, M. A. Puhan, E. Steyerberg, and T. Hothorn.
+&ldquo;Individual Participant Data Meta-analysis with 
+Mixed-effects Transformation Models&rdquo;.
+In: <em>Biostatistics</em> 23.4 (2022), pp. 1083&ndash;1098.
+DOI: <a href="https://doi.org/10.1093/biostatistics/kxab045">10.1093/biostatistics/kxab045</a>.</cite></p>
+
+<p><a id="bib-Sewak_Hothorn_2023"></a><a href="#cite-Sewak_Hothorn_2023">[12]</a><cite>
+A. Sewak and T. Hothorn.
+&ldquo;Estimating Transformations for Evaluating 
+Diagnostic Tests with Covariate Adjustment&rdquo;.
+In: <em>Statistical Methods in Medical Research</em> 32.7 (2023), pp. 1403&ndash;1419.
+DOI: <a href="https://doi.org/10.1177/09622802231176030">10.1177/09622802231176030</a>.</cite></p>
+
+<p><a id="bib-Siegfried_Kook_Hothorn_2023"></a><a href="#cite-Siegfried_Kook_Hothorn_2023">[13]</a><cite>
+S. Siegfried, L. Kook, and T. Hothorn.
+&ldquo;Distribution-Free Location-Scale Regression&rdquo;.
+In: <em>The American Statistician</em> 77.4 (2023), pp. 345&ndash;356.
+DOI: <a href="https://doi.org/10.1080/00031305.2023.2203177">10.1080/00031305.2023.2203177</a>.</cite></p>
+
+<p><a id="bib-Barbanti_Hothorn_2023"></a><a href="#cite-Barbanti_Hothorn_2023">[14]</a><cite>
+L. Barbanti and T. Hothorn.
+&ldquo;A Transformation Perspective on Marginal and Conditional Models&rdquo;.
+In: <em>Biostatistics</em> 25.2 (2024), pp. 402&ndash;428.
+DOI: <a href="https://doi.org/10.1093/biostatistics/kxac048">10.1093/biostatistics/kxac048</a>.</cite></p>
+
 
 			</div>
 

www/research/index.html

@@ -281,24 +281,24 @@ <h1 id="a-simple-transformation-model">A Simple Transformation Model</h1>
 ## 
 ## Coefficients:
 ##          Estimate Std. Error z value Pr(&gt;|z|)    
-## crim     0.082651   0.013712   6.028 1.66e-09 ***
-## zn      -0.010278   0.004923  -2.088   0.0368 *  
-## indus   -0.025199   0.021449  -1.175   0.2400    
-## nox      7.064875   1.491338   4.737 2.17e-06 ***
-## rm      -1.534745   0.218977  -7.009 2.41e-12 ***
-## age      0.008433   0.005118   1.648   0.0994 .  
-## dis      0.496536   0.077791   6.383 1.74e-10 ***
-## rad     -0.120767   0.024924  -4.845 1.26e-06 ***
-## tax      0.006482   0.001334   4.861 1.17e-06 ***
-## ptratio  0.395812   0.048312   8.193 2.22e-16 ***
-## b       -0.006451   0.001112  -5.803 6.52e-09 ***
-## lstat    0.281504   0.026502  10.622  &lt; 2e-16 ***
+## crim     0.082653   0.013711   6.028 1.66e-09 ***
+## zn      -0.010294   0.004923  -2.091   0.0365 *  
+## indus   -0.025192   0.021449  -1.175   0.2402    
+## nox      7.066574   1.491329   4.738 2.15e-06 ***
+## rm      -1.534753   0.218987  -7.008 2.41e-12 ***
+## age      0.008425   0.005118   1.646   0.0997 .  
+## dis      0.496597   0.077793   6.384 1.73e-10 ***
+## rad     -0.120737   0.024923  -4.844 1.27e-06 ***
+## tax      0.006483   0.001334   4.861 1.17e-06 ***
+## ptratio  0.395890   0.048313   8.194 2.22e-16 ***
+## b       -0.006451   0.001112  -5.802 6.53e-09 ***
+## lstat    0.281496   0.026501  10.622  &lt; 2e-16 ***
 ## ---
 ## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
 ## 
 ## Log-Likelihood:
 ##  -1297.487 (df = 26)
-## Likelihood-ratio Test: Chisq = 879.7666 on 12 degrees of freedom; p = &lt; 2.2e-16
+## Likelihood-ratio Test: Chisq = 879.7665 on 12 degrees of freedom; p = &lt; 2.2e-16
 </code></pre></div></div>
 <p>Two separate transformation function are fitted (for houses near and off
 Charles river, variable chas). The remaining variables enter a linear