Package: PEPBVS
Type: Package
Title: Bayesian Variable Selection using Power-Expected-Posterior Prior
Version: 1.0
Date: 2023-09-14
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 <xarmpi.kon@gmail.com>
Description: 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.
License: GPL (>= 2)
Imports: Matrix, Rcpp (>= 1.0.9)
LinkingTo: Rcpp, RcppArmadillo, RcppGSL
SystemRequirements: GNU GSL
Encoding: UTF-8
RoxygenNote: 7.2.1
Depends: R (>= 2.10)
LazyData: true
NeedsCompilation: yes
Packaged: 2023-09-15 15:09:54 UTC; k.charbi
Author: Konstantina Charmpi [aut, cre],
  Dimitris Fouskakis [aut],
  Ioannis Ntzoufras [aut]
Repository: CRAN
Date/Publication: 2023-09-19 16:40:02 UTC
Built: R 4.2.3; x86_64-w64-mingw32; 2024-04-24 01:09:41 UTC; windows
ExperimentalWindowsRuntime: ucrt
Archs: x64
