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# CITATION file created with {cffr} R package
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cff-version: 1.2.0
message: 'To cite package "PEPBVS" in publications use:'
type: software
license: GPL-2.0-or-later
title: 'PEPBVS: Bayesian Variable Selection using Power-Expected-Posterior Prior'
version: '1.0'
doi: 10.32614/CRAN.package.PEPBVS
abstract: 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)
  <https://doi.org/ 10.1214/21-BA1288>, Fouskakis and Ntzoufras (2020) <https://doi.org/
  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) <https://doi.org/ 10.2307/1403615>). Complementary functions for
  making predictions, as well as plotting and printing the results are also provided.
authors:
- family-names: Charmpi
  given-names: Konstantina
  email: xarmpi.kon@gmail.com
- family-names: Fouskakis
  given-names: Dimitris
  email: fouskakis@math.ntua.gr
- family-names: Ntzoufras
  given-names: Ioannis
  email: ntzoufras@aueb.gr
repository: https://CRAN.R-project.org/package=PEPBVS
date-released: '2023-09-14'
contact:
- family-names: Charmpi
  given-names: Konstantina
  email: xarmpi.kon@gmail.com