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@@ -392,27 +392,6 @@ <h6 class="cs-title">Journals</h6>
                             <span class="cs-publication-year">2024</span>
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                                 <div class="read-more-text"><p>We make Bayesian Additive Regression Networks (BARN) available as a Python package, <code>barmpy</code>, with documentation at this <a href="https://dvbuntu.github.io/barmpy/">url</a> for general machine learning practitioners. Our object-oriented design is compatible with SciKit-Learn, allowing usage of their tools like cross-validation. To ease learning to use <code>barmpy</code>, we produce a companion tutorial that expands on reference information in the documentation. Any interested user can <code>pip install barmpy</code> from the official <code>PyPi</code> repository. <code>barmpy</code> also serves as a baseline Python library for generic Bayesian Additive Regression Models.</p>
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-                                <div class="cs-item-social"><a href="/assets/documents/publications/2404.04738.pdf" target="_blank"><img src="../assets/icons/pdf.svg" alt="pdfDocument" width="30" height="30" decoding="async" aria-hidden="true"></a><a href="https://arxiv.org/abs/2404.04738" target="_blank"><img src="../assets/icons/link.svg" alt="webLink" width="30" height="30" decoding="async" aria-hidden="true"></a></div></div>
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-                            <span class="cs-publication-type">arXiv</span>
-                            <span class="cs-name"><p>BARMPy: Bayesian Additive Regression Models Python Package</p>
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-                            <span class="cs-publication-author"><span class="cs-highlight-author">Danielle Van Boxel</span></span>
-                            <span class="cs-publication-year">2024</span>
-                            <span class="cs-desc">
-                                <div class="read-more-text"><p>We make Bayesian Additive Regression Networks (BARN) available as a Python package, <code>barmpy</code>, with documentation at this <a href="https://dvbuntu.github.io/barmpy/">url</a> for general machine learning practitioners. Our object-oriented design is compatible with SciKit-Learn, allowing usage of their tools like cross-validation. To ease learning to use <code>barmpy</code>, we produce a companion tutorial that expands on reference information in the documentation. Any interested user can <code>pip install barmpy</code> from the official <code>PyPi</code> repository. <code>barmpy</code> also serves as a baseline Python library for generic Bayesian Additive Regression Models.</p>
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