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  "Title": "Sparse High-Dimensional Linear Regression with a PaRtitiOned\nEmpirical Bayes Ecm (PROBE) Algorithm",
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  "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.",
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  ]
}