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 gmail.com>
License:	GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation:	yes
SystemRequirements:	GNU GSL
CRAN checks:	PEPBVS results

Documentation:

Reference manual:

PEPBVS.pdf

Downloads:

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

Linking:

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