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Within this field, Gaussian graphical models aim to identify the pairs of variables whose dependence is maintained even after conditioning on the remaining variables in the data, known as the\n            <jats:italic>conditional dependence structure<\/jats:italic>\n            of the data. There are many existing software packages for Gaussian graphical modeling, however, they often make restrictive assumptions that reduce their flexibility for modeling data that are not identically distributed. Conversely,\n            <jats:monospace>covdepGE<\/jats:monospace>\n            is an R implementation of a variational weighted pseudo-likelihood algorithm for modeling the conditional dependence structure as a continuous function of an extraneous covariate. To build on the efficiency of this algorithm,\n            <jats:monospace>covdepGE<\/jats:monospace>\n            leverages parallelism and C++ integration with R. Additionally,\n            <jats:monospace>covdepGE<\/jats:monospace>\n            provides fully-automated and data-driven hyperparameter specification while maintaining flexibility for the user to decide key components of the estimation procedure. Through an extensive simulation study spanning diverse settings,\n            <jats:monospace>covdepGE<\/jats:monospace>\n            is demonstrated to be top of its class in recovering the ground truth conditional dependence structure while efficiently managing computational overhead.\n          <\/jats:p>","DOI":"10.1145\/3659206","type":"journal-article","created":{"date-parts":[[2024,4,30]],"date-time":"2024-04-30T13:39:12Z","timestamp":1714484352000},"page":"1-32","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Algorithm 1045: A Covariate-Dependent Approach to Gaussian Graphical Modeling in R"],"prefix":"10.1145","volume":"50","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7718-7449","authenticated-orcid":false,"given":"Jacob","family":"Helwig","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, Texas A&amp;M University, College Station, Texas, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5818-8494","authenticated-orcid":false,"given":"Sutanoy","family":"Dasgupta","sequence":"additional","affiliation":[{"name":"Department of Statistics, Texas A&amp;M University, College Station, Texas, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7043-0389","authenticated-orcid":false,"given":"Peng","family":"Zhao","sequence":"additional","affiliation":[{"name":"Department of Applied Economics and Statistics, University of Delaware, Newark, Delaware, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1776-9839","authenticated-orcid":false,"given":"Bani K.","family":"Mallick","sequence":"additional","affiliation":[{"name":"Department of Statistics, Texas A&amp;M University, College Station, Texas, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5345-8635","authenticated-orcid":false,"given":"Debdeep","family":"Pati","sequence":"additional","affiliation":[{"name":"Department of Statistics, Texas A&amp;M University, College Station, Texas, USA"}]}],"member":"320","published-online":{"date-parts":[[2024,6,28]]},"reference":[{"key":"e_1_3_3_2_1","doi-asserted-by":"publisher","DOI":"10.1214\/aos\/1176345986"},{"key":"e_1_3_3_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jmva.2019.03.005"},{"key":"e_1_3_3_4_1","doi-asserted-by":"publisher","DOI":"10.2307\/2987782"},{"key":"e_1_3_3_5_1","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.2017.1285773"},{"key":"e_1_3_3_6_1","doi-asserted-by":"publisher","DOI":"10.1214\/12-BA703"},{"key":"e_1_3_3_7_1","doi-asserted-by":"publisher","unstructured":"Peter Carbonetto Xiang Zhou and Matthew Stephens. 2017. varbvs: Fast Variable Selection for Large-Scale Regression. arXiv:1709.06597. 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