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        <font face="arial" size="2"><b>
                <font color="#003366">FELIX RIOS: <br>PUBLICATIONS, SUBMISSIONS, AND
                    WORKING PAPERS
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        <a href="index.html" target="_top">HOME</a> &#8195; &#8195;
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                <!-- <p>Research was partially supported by grants from AFOSR, NIH, NSF and ONR (MURI). </p> -->
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                    <dt>
                        <font face="arial" size="2"><b>
                                <font color="#000099">(S-4):</font>
                            </b>&nbsp;

                            <a href="https://github.com/felixleopoldo/dncDagger" target="_blank">[code]</a> <a class="reference external" href="https://GitHub.com/felixleopoldo/dncDagger/stargazers/" target="_blank"><img alt="GitHub stars" src="https://img.shields.io/github/stars/felixleopoldo/dncDagger.svg?style=social&amp;label=Star&amp;maxAge=2592000" /></a> &nbsp; &nbsp;

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                    <dt>
                        <font face="arial" size="2"><b>"dncDagger"</b>,
                            A C++ program interfaced in R for so-called exact learning of DAG models (Bayesian networks), showing that exact discovery is polynomial for certain sparse DAGs (e.g. matchings).
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                        <font face="arial" size="2"><b>
                                <font color="#000099">(S-3):</font>
                            </b>&nbsp;
                            <a href="https://cstrees.readthedocs.io/en/latest/" target="_blank">[docs]</a>
                            <a href="https://github.com/felixleopoldo/cstrees" target="_blank">[code]</a> <a class="reference external" href="https://GitHub.com/felixleopoldo/cslearn/stargazers/" target="_blank"><img alt="GitHub stars" src="https://img.shields.io/github/stars/felixleopoldo/cslearn.svg?style=social&amp;label=Star&amp;maxAge=2592000" /></a> &nbsp; &nbsp;

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                        <font face="arial" size="2"><b>"CStrees"</b>,
                            A Python package for context-specific graphical models
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                        <font face="arial" size="2"><b>
                                <font color="#000099">(S-2):</font>
                            </b>&nbsp;
                            <a href="https://benchpressdocs.readthedocs.io/en/latest/" target="_blank">[docs]</a>
                            <a href="https://github.com/felixleopoldo/benchpress" target="_blank">[code]</a> <a class="reference external" href="https://GitHub.com/felixleopoldo/benchpress/stargazers/" target="_blank"><img alt="GitHub stars" src="https://img.shields.io/github/stars/felixleopoldo/benchpress.svg?style=social&amp;label=Star&amp;maxAge=2592000" /></a> &nbsp;
                            &nbsp;
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                    <dt>
                        <font face="arial" size="2"><b>"Benchpress"</b>
                                 Benchpress started out from an idea to solve the benchmarking problem for causal discovery and structure learning once and for all.
                                 However, it has grown into a platform that also enables practitioners and researchers to get access to state-of-the-art open source algorithms that can be used for
                                 analysis without any coding. It requires minimal installation and scale seamlessly thanks to <a href="https://snakemake.readthedocs.io/en/stable/" target="_blank">Snakemake</a>.

                            </br>

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                        <font face="arial" size="2"><b>

                                <font color="#000099">(S-1):</font>
                            </b>&nbsp;
                            <a href="https://github.com/felixleopoldo/trilearn" target="_blank">[code]</a> <a class="reference external" href="https://GitHub.com/felixleopoldo/trilearn/stargazers/" target="_blank"><img alt="GitHub stars" src="https://img.shields.io/github/stars/felixleopoldo/trilearn.svg?style=social&amp;label=Star&amp;maxAge=2592000" /></a> &nbsp; &nbsp;
                        </font>
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                    <dt>
                        <font face="arial" size="2"><b>"trilearn"</b>,
                            A Python package for decomposable graphical models including structure learning and
                            classification
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                    <font face="arial" size="2">REPORT REQUESTS are to be directed to: </font>
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                    <font face="arial" size="2">&nbsp; </font>
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                        <font face="arial" size="2"> Felix Rios (felix.leopoldo.rios@gmail.com)
                            Stockholm, Sweden </font>
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