{ "_id": "6a4791968b9f0d9747cf1d13", "Package": "probe", "Type": "Package", "Title": "Sparse High-Dimensional Linear Regression with a PaRtitiOned\nEmpirical Bayes Ecm (PROBE) Algorithm", "Version": "1.3", "Date": "2026-02-27", "Authors@R": "c(person(\"Alexander\", \"McLain\", email = \"mclaina@mailbox.sc.edu\", role = c(\"aut\", \"cre\"), comment = c(ORCID = \"0000-0002-5475-0670\")), person(\"Anja\", \"Zgodic\", role = c(\"aut\", \"ctb\")))", "Description": "Implements an efficient and powerful Bayesian approach for\nsparse high-dimensional linear regression. It uses minimal\nprior assumptions on the parameters through plug-in empirical\nBayes estimates of hyperparameters. An efficient\nParameter-Expanded Expectation-Conditional-Maximization\n(PX-ECM) algorithm estimates maximum a posteriori (MAP) values\nof regression parameters and variable selection probabilities.\nThe PX-ECM results in a robust computationally efficient\ncoordinate-wise optimization, which adjusts for the impact of\nother predictor variables. The E-step is motivated by the\npopular two-group approach to multiple testing. The result is a\nPaRtitiOned empirical Bayes Ecm (PROBE) algorithm applied to\nsparse high-dimensional linear regression, implemented using\none-at-a-time or all-at-once type optimization. Simulation\nstudies found the all-at-once variant to be superior.", "BugReports": "https://github.com/alexmclain/PROBE/issues", "License": "GPL (>= 2)", "Encoding": "UTF-8", "RoxygenNote": "7.3.3", "NeedsCompilation": "yes", "Packaged": { "Date": "2026-07-02 09:22:59 UTC", "User": "root" }, "Repository": "https://alexmclain.r-universe.dev", "Date/Publication": "2026-02-27 17:38:31 UTC", "RemoteUrl": "https://github.com/alexmclain/probe", "RemoteRef": "HEAD", "RemoteSha": "79045968c6497f936e4f5c1b8fc75cab0e75c5a6", "RemoteSubdir": "probe", "Author": "Alexander McLain [aut, cre] (ORCID:\n),\nAnja Zgodic [aut, ctb]", "Maintainer": "Alexander McLain ", "_user": "alexmclain", "_type": "src", "_file": "probe_1.3.tar.gz", "_fileid": "https://r2.ropensci.org/7e5c138e44d4b48b6cc0df24b7ca79bcc1778184413f9de638f08da2f51be4e5", "_filesize": 870380, "_sha256": "7e5c138e44d4b48b6cc0df24b7ca79bcc1778184413f9de638f08da2f51be4e5", "_expires": "2026-10-11T10:40:22.000Z", "_created": 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