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", role=c("aut","cre")), person("Dimitris", "Fouskakis", email="fouskakis@math.ntua.gr", role="aut"), person("Ioannis", "Ntzoufras", email="ntzoufras@aueb.gr", role="aut")) Maintainer: Konstantina Charmpi Description: Performs Bayesian variable selection under normal linear models for the data with the model parameters following as prior distributions either the power-expected-posterior (PEP) or the intrinsic (a special case of the former) (Fouskakis and Ntzoufras (2022) , Fouskakis and Ntzoufras (2020) ). The prior distribution on model space is the uniform over all models or the uniform on model dimension (a special case of the beta-binomial prior). The selection is performed by either implementing a full enumeration and evaluation of all possible models or using the Markov Chain Monte Carlo Model Composition (MC3) algorithm (Madigan and York (1995) ). Complementary functions for hypothesis testing, estimation and predictions under Bayesian model averaging, as well as, plotting and printing the results are also provided. The results can be compared to the ones obtained under other well-known priors on model parameters and model spaces. License: GPL (>= 2) Imports: BAS, BayesVarSel, Matrix, mcmcse, mvtnorm, Rcpp (>= 1.0.9) LinkingTo: Rcpp, RcppArmadillo, RcppGSL SystemRequirements: GNU GSL Encoding: UTF-8 RoxygenNote: 7.3.2 Depends: R (>= 2.10) Config/pak/sysreqs: cmake libfftw3-dev make libgsl0-dev libuv1-dev Repository: https://kcharmpi.r-universe.dev Date/Publication: 2025-12-31 11:25:12 UTC RemoteUrl: https://github.com/kcharmpi/pepbvs RemoteRef: HEAD RemoteSha: d28a37ccc4bdf97478dcb417c67a510427329541 NeedsCompilation: yes Packaged: 2026-07-04 02:28:49 UTC; root Author: Konstantina Charmpi [aut, cre], Dimitris Fouskakis [aut], Ioannis Ntzoufras [aut]