deploy: c0e5a7a

Author: robjhyndman

articles.html

@@ -2357,6 +2357,39 @@
 <div class="posts-container posts-apply-limit l-page">
 <div class="posts-list">
 <h1 class="posts-list-caption" data-caption="All articles">All articles</h1>
+<a href="articles/RJ-2025-029/" class="post-preview">
+<script class="post-metadata" type="text/json">{"categories":[]}</script>
+<div class="metadata">
+<div class="publishedDate">Nov. 24, 2025</div>
+<div class="dt-authors">
+<div class="dt-author">Marco Alfó</div>
+<div class="dt-author">Maria Francesca Marino</div>
+<div class="dt-author">Maria Giovanna Ranalli</div>
+<div class="dt-author">Nicola Salvati</div>
+</div>
+</div>
+<div class="thumbnail">
+<img/>
+</div>
+<div class="description">
+<h2>lqmix: an R Package for Longitudinal Data Analysis via Linear Quantile Mixtures</h2>
+<div class="dt-tags"></div>
+<p>The analysis of longitudinal data gives the chance to observe how units'
+behavior changes over time, but it also poses a series of issues.
+These have been the focus of an extensive literature in the context of
+linear and generalized linear regression, moving also, in the last ten
+years or so, to the context of linear quantile regression for
+continuous responses. In this paper, we present `lqmix`, a novel `R`
+package that assists in estimating a class of linear quantile
+regression models for longitudinal data, in the presence of
+time-constant and/or time-varying, unit-specific, random coefficients,
+with unspecified distribution. Model parameters are estimated in a
+maximum likelihood framework via an extended EM algorithm, while
+the corresponding standard errors are derived via a block-bootstrap
+procedure. The analysis of a benchmark dataset is used to give details
+on the package functions.</p>
+</div>
+</a>
 <a href="articles/RJ-2025-031/" class="post-preview">
 <script class="post-metadata" type="text/json">{"categories":[]}</script>
 <div class="metadata">
@@ -2597,39 +2630,6 @@ <h2>Rendering LaTeX in R</h2>
 <p>The xdvir package provides functions for rendering  LaTeX fragments as labels, annotations, and data symbols in R plots.  There are convenient high-level functions for rendering LaTeX fragments, including labels on ggplot2 plots, plus lower-level functions for more fine control over the separate authoring, typesetting, and rendering steps. There is support for making use of LaTeX packages, including TikZ graphics.  The rendered LaTeX output is fully integrated with R graphics output in the sense that LaTeX output  can be positioned and sized relative to R graphics output and vice versa.</p>
 </div>
 </a>
-<a href="articles/RJ-2025-029/" class="post-preview">
-<script class="post-metadata" type="text/json">{"categories":[]}</script>
-<div class="metadata">
-<div class="publishedDate">Oct. 21, 2025</div>
-<div class="dt-authors">
-<div class="dt-author">Marco Alfó</div>
-<div class="dt-author">Maria Francesca Marino</div>
-<div class="dt-author">Maria Giovanna Ranalli</div>
-<div class="dt-author">Nicola Salvati</div>
-</div>
-</div>
-<div class="thumbnail">
-<img/>
-</div>
-<div class="description">
-<h2>lqmix: an R Package for Longitudinal Data Analysis via Linear Quantile Mixtures</h2>
-<div class="dt-tags"></div>
-<p>The analysis of longitudinal data gives the chance to observe how unit
-behaviors change over time, but it also poses a series of issues.
-These have been the focus of an extensive literature in the context of
-linear and generalized linear regression, moving also, in the last ten
-years or so, to the context of linear quantile regression for
-continuous responses. In this paper, we present `lqmix`, a novel `R`
-package that assists in estimating a class of linear quantile
-regression models for longitudinal data, in the presence of
-time-constant and/or time-varying, unit-specific, random coefficients,
-with unspecified distribution. Model parameters are estimated in a
-maximum likelihood framework via an extended EM algorithm, while
-parameters' standard errors are derived via a block-bootstrap
-procedure. The analysis of a benchmark dataset is used to give details
-on the package functions.</p>
-</div>
-</a>
 <a href="articles/RJ-2025-011/" class="post-preview">
 <script class="post-metadata" type="text/json">{"categories":[]}</script>
 <div class="metadata">

articles.xml

@@ -11,7 +11,31 @@
       <link>https://journal.r-project.org/</link>
     </image>
     <generator>Distill</generator>
-    <lastBuildDate>Fri, 24 Oct 2025 00:00:00 +0000</lastBuildDate>
+    <lastBuildDate>Mon, 24 Nov 2025 00:00:00 +0000</lastBuildDate>
+    <item>
+      <title>lqmix: an R Package for Longitudinal Data Analysis via Linear Quantile Mixtures</title>
+      <dc:creator>Marco Alfó</dc:creator>
+      <dc:creator>Maria Francesca Marino</dc:creator>
+      <dc:creator>Maria Giovanna Ranalli</dc:creator>
+      <dc:creator>Nicola Salvati</dc:creator>
+      <link>https://journal.r-project.org/articles/RJ-2025-029</link>
+      <description>The analysis of longitudinal data gives the chance to observe how units'
+behavior changes over time, but it also poses a series of issues.
+These have been the focus of an extensive literature in the context of
+linear and generalized linear regression, moving also, in the last ten
+years or so, to the context of linear quantile regression for
+continuous responses. In this paper, we present `lqmix`, a novel `R`
+package that assists in estimating a class of linear quantile
+regression models for longitudinal data, in the presence of
+time-constant and/or time-varying, unit-specific, random coefficients,
+with unspecified distribution. Model parameters are estimated in a
+maximum likelihood framework via an extended EM algorithm, while
+the corresponding standard errors are derived via a block-bootstrap
+procedure. The analysis of a benchmark dataset is used to give details
+on the package functions.</description>
+      <guid>https://journal.r-project.org/articles/RJ-2025-029</guid>
+      <pubDate>Mon, 24 Nov 2025 00:00:00 +0000</pubDate>
+    </item>
     <item>
       <title>moonboot: An R Package Implementing m-out-of-n Bootstrap Methods</title>
       <dc:creator>Christoph Dalitz</dc:creator>
@@ -165,30 +189,6 @@ cases.</description>
       <pubDate>Tue, 21 Oct 2025 00:00:00 +0000</pubDate>
       <media:content url="https://journal.r-project.org/articles/RJ-2025-028/RJ-2025-028_files/figure-html5/typesetting-1.png" medium="image" type="image/png" width="1344" height="864"/>
     </item>
-    <item>
-      <title>lqmix: an R Package for Longitudinal Data Analysis via Linear Quantile Mixtures</title>
-      <dc:creator>Marco Alfó</dc:creator>
-      <dc:creator>Maria Francesca Marino</dc:creator>
-      <dc:creator>Maria Giovanna Ranalli</dc:creator>
-      <dc:creator>Nicola Salvati</dc:creator>
-      <link>https://journal.r-project.org/articles/RJ-2025-029</link>
-      <description>The analysis of longitudinal data gives the chance to observe how unit
-behaviors change over time, but it also poses a series of issues.
-These have been the focus of an extensive literature in the context of
-linear and generalized linear regression, moving also, in the last ten
-years or so, to the context of linear quantile regression for
-continuous responses. In this paper, we present `lqmix`, a novel `R`
-package that assists in estimating a class of linear quantile
-regression models for longitudinal data, in the presence of
-time-constant and/or time-varying, unit-specific, random coefficients,
-with unspecified distribution. Model parameters are estimated in a
-maximum likelihood framework via an extended EM algorithm, while
-parameters' standard errors are derived via a block-bootstrap
-procedure. The analysis of a benchmark dataset is used to give details
-on the package functions.</description>
-      <guid>https://journal.r-project.org/articles/RJ-2025-029</guid>
-      <pubDate>Tue, 21 Oct 2025 00:00:00 +0000</pubDate>
-    </item>
     <item>
       <title>The CRAN Task View Initiative</title>
       <dc:creator>Achim Zeileis</dc:creator>

articles/RJ-2025-029/RJ-2025-029.pdf

@@ -13,7 +13,7 @@
 \volnumber{3}
 \year{2025}
 \month{September}
-\setcounter{page}{188}
+\setcounter{page}{1}
 
 %% replace RJtemplate with your article
 \begin{article}