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‎articles.html‎

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<div class="posts-container posts-apply-limit l-page">
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<div class="posts-list">
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<h1 class="posts-list-caption" data-caption="All articles">All articles</h1>
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<a href="articles/RJ-2025-035/" class="post-preview">
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<script class="post-metadata" type="text/json">{"categories":[]}</script>
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<div class="metadata">
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<div class="publishedDate">Feb. 18, 2026</div>
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<div class="dt-authors">
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<div class="dt-author">Sonia Pérez-Fernández</div>
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<div class="dt-author">Pablo Martínez-Camblor</div>
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<div class="dt-author">Norberto Corral-Blanco</div>
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</div>
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</div>
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<div class="thumbnail">
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<img src="articles/RJ-2025-035/figures/movieROC_mainFunctions.png"/>
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</div>
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<div class="description">
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<h2>movieROC: Visualizing the Decision Rules Underlying Binary Classification</h2>
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<div class="dt-tags"></div>
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<p>The receiver operating characteristic (ROC) curve is a graphical tool
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commonly used to depict the binary classification accuracy of a
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continuous marker in terms of its sensitivity and specificity. The
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standard ROC curve assumes a monotone relationship between the marker
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and the response, inducing classification subsets of the form
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$(c,\infty)$ with $c \in \mathbb{R}$. However, in non-standard cases,
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the involved classification regions are not so clear, highlighting the
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importance of tracking the decision rules. This paper introduces the R
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package movieROC,
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which provides visualization tools for understanding the ability of
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markers to identify a characteristic of interest, complementing the
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ROC curve representation. This tool accommodates multivariate
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scenarios and generalizations involving different decision rules. The
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main contribution of this package is the visualization of the
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underlying classification regions, with the associated gain in
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interpretability. Adding the time (videos) as a third dimension, this
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package facilitates the visualization of binary classification in
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multivariate problems. It constitutes a good tool to generate
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graphical material for presentations.</p>
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</div>
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</a>
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<a href="articles/RJ-2025-042/" class="post-preview">
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<script class="post-metadata" type="text/json">{"categories":[]}</script>
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<div class="metadata">
@@ -2423,43 +2460,6 @@ <h2>ASML: An R Package for Algorithm Selection with Machine Learning</h2>
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<p>For extensively studied computational problems, it is commonly acknowledged that different instances may require different algorithms for optimal performance. The R package ASML focuses on the task of efficiently selecting from a given portfolio of algorithms, the most suitable one for each specific problem instance, based on significant instance features. The package allows for the use of the machine learning tools available in the R package caret and additionally offers visualization tools and summaries of results that make it easier to interpret how algorithm selection techniques perform, helping users better understand and assess their behavior and performance improvements.</p>
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</div>
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</a>
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<a href="articles/RJ-2025-035/" class="post-preview">
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<script class="post-metadata" type="text/json">{"categories":[]}</script>
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<div class="metadata">
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<div class="publishedDate">Feb. 4, 2026</div>
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<div class="dt-authors">
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<div class="dt-author">Sonia Pérez-Fernández</div>
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<div class="dt-author">Pablo Martínez-Camblor</div>
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<div class="dt-author">Norberto Corral-Blanco</div>
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</div>
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</div>
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<div class="thumbnail">
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<img src="articles/RJ-2025-035/figures/movieROC_mainFunctions.png"/>
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</div>
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<div class="description">
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<h2>movieROC: Visualizing the Decision Rules Underlying Binary Classification</h2>
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<div class="dt-tags"></div>
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<p>The receiver operating characteristic (ROC) curve is a graphical tool
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commonly used to depict the binary classification accuracy of a
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continuous marker in terms of its sensitivity and specificity. The
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standard ROC curve assumes a monotone relationship between the marker
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and the response, inducing classification subsets of the form
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$(c,\infty)$ with $c \in \mathbb{R}$. However, in non-standard cases,
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the involved classification regions are not so clear, highlighting the
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importance of tracking the decision rules. This paper introduces the R
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package movieROC,
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which provides visualization tools for understanding the ability of
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markers to identify a characteristic of interest, complementing the
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ROC curve representation. This tool accommodates multivariate
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scenarios and generalizations involving different decision rules. The
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main contribution of this package is the visualization of the
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underlying classification regions, with the associated gain in
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interpretability. Adding the time (videos) as a third dimension, this
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package facilitates the visualization of binary classification in
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multivariate problems. It constitutes a good tool to generate
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graphical material for presentations.</p>
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</div>
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</a>
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<a href="articles/RJ-2025-036/" class="post-preview">
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<script class="post-metadata" type="text/json">{"categories":[]}</script>
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<div class="metadata">

‎articles.xml‎

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<link>https://journal.r-project.org/</link>
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</image>
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<generator>Distill</generator>
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<lastBuildDate>Wed, 11 Feb 2026 00:00:00 +0000</lastBuildDate>
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<lastBuildDate>Wed, 18 Feb 2026 00:00:00 +0000</lastBuildDate>
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<item>
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<title>movieROC: Visualizing the Decision Rules Underlying Binary Classification</title>
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<dc:creator>Sonia Pérez-Fernández</dc:creator>
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<dc:creator>Pablo Martínez-Camblor</dc:creator>
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<dc:creator>Norberto Corral-Blanco</dc:creator>
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<link>https://journal.r-project.org/articles/RJ-2025-035</link>
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<description>The receiver operating characteristic (ROC) curve is a graphical tool
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commonly used to depict the binary classification accuracy of a
23+
continuous marker in terms of its sensitivity and specificity. The
24+
standard ROC curve assumes a monotone relationship between the marker
25+
and the response, inducing classification subsets of the form
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$(c,\infty)$ with $c \in \mathbb{R}$. However, in non-standard cases,
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the involved classification regions are not so clear, highlighting the
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importance of tracking the decision rules. This paper introduces the R
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package movieROC,
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which provides visualization tools for understanding the ability of
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markers to identify a characteristic of interest, complementing the
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ROC curve representation. This tool accommodates multivariate
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scenarios and generalizations involving different decision rules. The
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main contribution of this package is the visualization of the
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underlying classification regions, with the associated gain in
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interpretability. Adding the time (videos) as a third dimension, this
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package facilitates the visualization of binary classification in
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multivariate problems. It constitutes a good tool to generate
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graphical material for presentations.</description>
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<guid>https://journal.r-project.org/articles/RJ-2025-035</guid>
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<pubDate>Wed, 18 Feb 2026 00:00:00 +0000</pubDate>
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<media:content url="https://journal.r-project.org/articles/RJ-2025-035/figures/movieROC_mainFunctions.png" medium="image" type="image/png" width="971" height="306"/>
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</item>
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<item>
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<title>GeRnika: An R Package for the Simulation, Visualization and Comparison of Tumor Phylogenies</title>
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<dc:creator>Aitor Sánchez-Ferrera</dc:creator>
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<guid>https://journal.r-project.org/articles/RJ-2025-045</guid>
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<pubDate>Tue, 10 Feb 2026 00:00:00 +0000</pubDate>
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</item>
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<item>
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<title>movieROC: Visualizing the Decision Rules Underlying Binary Classification</title>
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<dc:creator>Sonia Pérez-Fernández</dc:creator>
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<dc:creator>Pablo Martínez-Camblor</dc:creator>
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<dc:creator>Norberto Corral-Blanco</dc:creator>
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<link>https://journal.r-project.org/articles/RJ-2025-035</link>
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<description>The receiver operating characteristic (ROC) curve is a graphical tool
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commonly used to depict the binary classification accuracy of a
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continuous marker in terms of its sensitivity and specificity. The
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standard ROC curve assumes a monotone relationship between the marker
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and the response, inducing classification subsets of the form
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$(c,\infty)$ with $c \in \mathbb{R}$. However, in non-standard cases,
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the involved classification regions are not so clear, highlighting the
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importance of tracking the decision rules. This paper introduces the R
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package movieROC,
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which provides visualization tools for understanding the ability of
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markers to identify a characteristic of interest, complementing the
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ROC curve representation. This tool accommodates multivariate
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scenarios and generalizations involving different decision rules. The
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main contribution of this package is the visualization of the
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underlying classification regions, with the associated gain in
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interpretability. Adding the time (videos) as a third dimension, this
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package facilitates the visualization of binary classification in
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multivariate problems. It constitutes a good tool to generate
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graphical material for presentations.</description>
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<guid>https://journal.r-project.org/articles/RJ-2025-035</guid>
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<pubDate>Wed, 04 Feb 2026 00:00:00 +0000</pubDate>
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<media:content url="https://journal.r-project.org/articles/RJ-2025-035/figures/movieROC_mainFunctions.png" medium="image" type="image/png" width="971" height="306"/>
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</item>
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<item>
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<title>dtComb: A Comprehensive R Library and Web Tool for Combining Diagnostic Tests</title>
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<dc:creator>S. Ilayda Yerlitaş Taştan</dc:creator>

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