bnstruct: an R package for Bayesian Network structure learning in the presence of missing data

Alberto Franzin, Francesco Sambo, Barbara Di Camillo
<span title="2016-12-21">2016</span> <i title="Oxford University Press (OUP)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/wmo54ba2jnemdingjj4fl3736a" style="color: black;">Bioinformatics</a> </i> &nbsp;
Motivation: A Bayesian Network is a probabilistic graphical model that encodes probabilistic dependencies between a set of random variables. We introduce bnstruct, an open source R package to (i) learn the structure and the parameters of a Bayesian Network from data in the presence of missing values and (ii) perform reasoning and inference on the learned Bayesian Networks. To the best of our knowledge, there is no other open source software that provides methods for all of these tasks,
more &raquo; ... rly the manipulation of missing data, which is a common situation in practice.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1093/bioinformatics/btw807">doi:10.1093/bioinformatics/btw807</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/28003263">pmid:28003263</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/vkycag4nx5df7mxhvuf7s3bbfq">fatcat:vkycag4nx5df7mxhvuf7s3bbfq</a> </span>
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