{ "_id": "6ace3d98a14063b82c3b1bee", "Package": "PEPBVS", "Type": "Package", "Title": "Bayesian Variable Selection using Power-Expected-Posterior Prior", "Version": "2.2", "Date": "2025-09-29", "Authors@R": "c(person(\"Konstantina\", \"Charmpi\", email=\"xarmpi.kon@gmail.com\",\nrole=c(\"aut\",\"cre\")),\nperson(\"Dimitris\", \"Fouskakis\", email=\"fouskakis@math.ntua.gr\", role=\"aut\"),\nperson(\"Ioannis\", \"Ntzoufras\", email=\"ntzoufras@aueb.gr\", role=\"aut\"))", "Maintainer": "Konstantina Charmpi ", "Description": "Performs Bayesian variable selection under normal linear\nmodels for the data with the model parameters following as\nprior distributions either the power-expected-posterior (PEP)\nor the intrinsic (a special case of the former) (Fouskakis and\nNtzoufras (2022) , Fouskakis and\nNtzoufras (2020) ). The prior\ndistribution on model space is the uniform over all models or\nthe uniform on model dimension (a special case of the\nbeta-binomial prior). The selection is performed by either\nimplementing a full enumeration and evaluation of all possible\nmodels or using the Markov Chain Monte Carlo Model Composition\n(MC3) algorithm (Madigan and York (1995) ). Complementary functions for hypothesis\ntesting, estimation and predictions under Bayesian model\naveraging, as well as, plotting and printing the results are\nalso provided. 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