PEPBVS: Bayesian Variable Selection using Power-Expected-Posterior Prior

Performs Bayesian variable selection under normal linear models for the data with the model parameters following as prior either the power-expected-posterior (PEP) or the intrinsic (a special case of the former) (Fouskakis and Ntzoufras (2022) <doi:10.1214/21-BA1288>, Fouskakis and Ntzoufras (2020) <doi:10.3390/econometrics8020017>). The prior distribution on model space is the uniform on model space or the uniform on model dimension (a special case of the beta-binomial prior). The selection can be done either with full enumeration of all possible models or using the Markov Chain Monte Carlo Model Composition (MC3) algorithm (Madigan and York (1995) <doi:10.2307/1403615>). Complementary functions for making predictions, as well as plotting and printing the results are also provided.

Version: 1.0
Depends: R (≥ 2.10)
Imports: Matrix, Rcpp (≥ 1.0.9)
LinkingTo: Rcpp, RcppArmadillo, RcppGSL
Published: 2023-09-19
Author: Konstantina Charmpi [aut, cre], Dimitris Fouskakis [aut], Ioannis Ntzoufras [aut]
Maintainer: Konstantina Charmpi <xarmpi.kon at>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: yes
SystemRequirements: GNU GSL
CRAN checks: PEPBVS results


Reference manual: PEPBVS.pdf


Package source: PEPBVS_1.0.tar.gz
Windows binaries: r-prerel:, r-release:, r-oldrel:
macOS binaries: r-prerel (arm64): PEPBVS_1.0.tgz, r-release (arm64): PEPBVS_1.0.tgz, r-oldrel (arm64): PEPBVS_1.0.tgz, r-prerel (x86_64): PEPBVS_1.0.tgz, r-release (x86_64): PEPBVS_1.0.tgz


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