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 |
| DOI: |
10.32614/CRAN.package.PEPBVS |
| 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:
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