deploy: ed70024

Author: robjhyndman

articles.html

@@ -2356,6 +2356,40 @@
 <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-2025-007/" class="post-preview">
+<script class="post-metadata" type="text/json">{"categories":[]}</script>
+<div class="metadata">
+<div class="publishedDate">Aug. 11, 2025</div>
+<div class="dt-authors">
+<div class="dt-author">Seongwon Im</div>
+<div class="dt-author">Ander Wilson</div>
+<div class="dt-author">Daniel Mork</div>
+</div>
+</div>
+<div class="thumbnail">
+<img/>
+</div>
+<div class="description">
+<h2>Structured Bayesian Regression Tree Models for Estimating Distributed Lag Effects: The R Package dlmtree</h2>
+<div class="dt-tags"></div>
+<p>When examining the relationship between an exposure and an outcome,
+there is often a time lag between exposure and the observed effect on
+the outcome. A common statistical approach for estimating the
+relationship between the outcome and lagged measurements of exposure
+is a distributed lag model (DLM). Because repeated measurements are
+often autocorrelated, the lagged effects are typically constrained to
+vary smoothly over time. A recent statistical development on the
+smoothing constraint is a tree structured DLM framework. We present an
+R package dlmtree, available on CRAN, that integrates tree structured
+DLM and extensions into a comprehensive software package with
+user-friendly implementation. A conceptual background on tree
+structured DLMs and a demonstration of the fitting process of each
+model using simulated data are provided. We also demonstrate inference
+and interpretation using the fitted models, including summary and
+visualization. Additionally, a built-in shiny app for heterogeneity
+analysis is included.</p>
+</div>
+</a>
 <a href="articles/RJ-2025-001/" class="post-preview">
 <script class="post-metadata" type="text/json">{"categories":[]}</script>
 <div class="metadata">
@@ -2517,40 +2551,6 @@ <h2>latrend: A Framework for Clustering Longitudinal Data</h2>
 <p>Clustering of longitudinal data is used to explore common trends among subjects over time. In this paper, we focus on cases where the sole repeated measurement of interest is numeric. Various R packages have been introduced throughout the years for identifying clusters of longitudinal patterns, summarizing the variability in trajectories between subjects in terms of one or more trends. We introduce the R package latrend as a framework for the unified application of methods for longitudinal clustering, enabling comparisons between methods with minimal coding. The package also serves as an interface to commonly used packages for clustering longitudinal data, including dtwclust, flexmix, kml, lcmm, mclust, mixAK, and mixtools. This enables researchers to easily compare different approaches, implementations, and method specifications. Furthermore, researchers can build upon the standard tools provided by the framework to quickly implement new cluster methods, enabling rapid prototyping.</p>
 </div>
 </a>
-<a href="articles/RJ-2025-007/" class="post-preview">
-<script class="post-metadata" type="text/json">{"categories":[]}</script>
-<div class="metadata">
-<div class="publishedDate">Aug. 7, 2025</div>
-<div class="dt-authors">
-<div class="dt-author">Seongwon Im</div>
-<div class="dt-author">Ander Wilson</div>
-<div class="dt-author">Daniel Mork</div>
-</div>
-</div>
-<div class="thumbnail">
-<img/>
-</div>
-<div class="description">
-<h2>Structured Bayesian Regression Tree Models for Estimating Distributed Lag Effects: The R Package dlmtree</h2>
-<div class="dt-tags"></div>
-<p>When examining the relationship between an exposure and an outcome,
-there is often a time lag between exposure and the observed effect on
-the outcome. A common statistical approach for estimating the
-relationship between the outcome and lagged measurements of exposure
-is a distributed lag model (DLM). Because repeated measurements are
-often autocorrelated, the lagged effects are typically constrained to
-vary smoothly over time. A recent statistical development on the
-smoothing constraint is a tree structured DLM framework. We present an
-R package dlmtree, available on CRAN, that integrates tree structured
-DLM and extensions into a comprehensive software package with
-user-friendly implementation. A conceptual background on tree
-structured DLMs and a demonstration of the fitting process of each
-model using simulated data are provided. We also demonstrate inference
-and interpretation using the fitted models, including summary and
-visualization. Additionally, a built-in shiny app for heterogeneity
-analysis is included.</p>
-</div>
-</a>
 <a href="articles/RJ-2025-008/" class="post-preview">
 <script class="post-metadata" type="text/json">{"categories":[]}</script>
 <div class="metadata">

articles.xml

@@ -11,7 +11,32 @@
       <link>https://journal.r-project.org/</link>
     </image>
     <generator>Distill</generator>
-    <lastBuildDate>Thu, 07 Aug 2025 00:00:00 +0000</lastBuildDate>
+    <lastBuildDate>Mon, 11 Aug 2025 00:00:00 +0000</lastBuildDate>
+    <item>
+      <title>Structured Bayesian Regression Tree Models for Estimating Distributed Lag Effects: The R Package dlmtree</title>
+      <dc:creator>Seongwon Im</dc:creator>
+      <dc:creator>Ander Wilson</dc:creator>
+      <dc:creator>Daniel Mork</dc:creator>
+      <link>https://journal.r-project.org/articles/RJ-2025-007</link>
+      <description>When examining the relationship between an exposure and an outcome,
+there is often a time lag between exposure and the observed effect on
+the outcome. A common statistical approach for estimating the
+relationship between the outcome and lagged measurements of exposure
+is a distributed lag model (DLM). Because repeated measurements are
+often autocorrelated, the lagged effects are typically constrained to
+vary smoothly over time. A recent statistical development on the
+smoothing constraint is a tree structured DLM framework. We present an
+R package dlmtree, available on CRAN, that integrates tree structured
+DLM and extensions into a comprehensive software package with
+user-friendly implementation. A conceptual background on tree
+structured DLMs and a demonstration of the fitting process of each
+model using simulated data are provided. We also demonstrate inference
+and interpretation using the fitted models, including summary and
+visualization. Additionally, a built-in shiny app for heterogeneity
+analysis is included.</description>
+      <guid>https://journal.r-project.org/articles/RJ-2025-007</guid>
+      <pubDate>Mon, 11 Aug 2025 00:00:00 +0000</pubDate>
+    </item>
     <item>
       <title>SIREN: A Hybrid CFA-EFA R Package for Controlling Acquiescence in Restricted Factorial Solutions</title>
       <dc:creator>David Navarro-Gonzalez</dc:creator>
@@ -120,31 +145,6 @@ meningitis cases in an Italian region.</description>
       <guid>https://journal.r-project.org/articles/RJ-2025-006</guid>
       <pubDate>Thu, 07 Aug 2025 00:00:00 +0000</pubDate>
     </item>
-    <item>
-      <title>Structured Bayesian Regression Tree Models for Estimating Distributed Lag Effects: The R Package dlmtree</title>
-      <dc:creator>Seongwon Im</dc:creator>
-      <dc:creator>Ander Wilson</dc:creator>
-      <dc:creator>Daniel Mork</dc:creator>
-      <link>https://journal.r-project.org/articles/RJ-2025-007</link>
-      <description>When examining the relationship between an exposure and an outcome,
-there is often a time lag between exposure and the observed effect on
-the outcome. A common statistical approach for estimating the
-relationship between the outcome and lagged measurements of exposure
-is a distributed lag model (DLM). Because repeated measurements are
-often autocorrelated, the lagged effects are typically constrained to
-vary smoothly over time. A recent statistical development on the
-smoothing constraint is a tree structured DLM framework. We present an
-R package dlmtree, available on CRAN, that integrates tree structured
-DLM and extensions into a comprehensive software package with
-user-friendly implementation. A conceptual background on tree
-structured DLMs and a demonstration of the fitting process of each
-model using simulated data are provided. We also demonstrate inference
-and interpretation using the fitted models, including summary and
-visualization. Additionally, a built-in shiny app for heterogeneity
-analysis is included.</description>
-      <guid>https://journal.r-project.org/articles/RJ-2025-007</guid>
-      <pubDate>Thu, 07 Aug 2025 00:00:00 +0000</pubDate>
-    </item>
     <item>
       <title>CDsampling: An R Package for Constrained D-Optimal Sampling in Paid Research Studies</title>
       <dc:creator>Yifei Huang</dc:creator>