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PEPBVS

2.2

Bayesian Variable Selection using Power-Expected-Posterior Prior

0packages depend
2.8Kdownloads / year
test coverage
13/13checks pass

Overview

About
Maintained by Konstantina CharmpiFirst published 2023-09-194 releasesCRAN page ↗

Performs Bayesian variable selection under normal linear models for the data with the model parameters following as prior distributions 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 over all models or the uniform on model dimension (a special case of the beta-binomial prior). The selection is performed by either implementing a full enumeration and evaluation 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 hypothesis testing, estimation and predictions under Bayesian model averaging, as well as, plotting and printing the results are also provided. The results can be compared to the ones obtained under other well-known priors on model parameters and model spaces.

Install

Health

CRAN checks
13OK
Slowest check: 4.1 min · r-devel-windows-x86_64
Code health
None
Tests · ratio 0.00
not tracked
Coverage
100%
Documentation · exports
6
Dependencies · direct
Check history
  • OK2026-08-05
    13 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
  • NOTE2026-08-01
    12 OK · 1 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
  • OK2026-04-22
    14 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
  • ERROR2026-04-18
    13 OK · 0 NOTE · 0 WARNING · 1 ERROR · 0 FAILURE
  • OK2026-03-10
    14 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE

Documentation

Documentation
READMENoVignettesNopkgdown siteNoNEWSYes · 100% structuredCode of conductNoContributing guideNo
Examples that run
100%
Documented parameters
94%
Return-value docs
83%
References docs
58%

Downloads

2.8K
CRAN downloads in the past year
Rank #20,417 · ~8/day · ~231/mo
Daily download trend is not available in this view yet.
10330 days
54290 days
2.8K1 year
Compare downloads with other packages →
Also on403 r2u12 autocran

Dependencies

Declared dependencies
8 external dependencies (excludes base and recommended)
Depends (1)
R >= 2.10
Suggests (0)
none
Enhances (0)
none
Reverse dependencies
0direct
0indirect

Nothing depends on this yet.

Code & Tests

Datasets

People & History

People (3)
Maintainer (1)
Author, Maintainer
Authors (3)
Author, Maintainer
Package Timeline

4 releases. Pick two to compare their code metrics. R releases are shown for context.

  • R
    R 4.6.0 released · 2026-04-24
  • 2.2Latest
    2025-09-29 · current release · diff ↗
  • R
    R 4.5.0 released · 2025-04-11
  • 2.1
    2024-11-12 · diff ↗
  • 2.0
    2024-10-30 · diff ↗
  • R
    R 4.4.0 released · 2024-04-24
  • 1.0
    2023-09-19
  • R
    R 4.3.0 released · 2023-04-21

Package metadata

First published
2023-09-19
Total releases
4 / 3 yrs
License
GPL (>= 2) OSI
Minimum R
≥ 2.10
Bundled data
2.1 KB / 1 file
Download size
45 KB
Installed size
not tracked yet
With dependencies
not tracked yet

Cite

Cite this package

Run in R for the authors' preferred citation:

citation("PEPBVS")
Charmpi, K., Fouskakis, D., & Ntzoufras, I. (2025). PEPBVS: Bayesian Variable Selection using Power-Expected-Posterior Prior (Version 2.2) [Computer software]. https://doi.org/10.32614/CRAN.package.PEPBVS

This is what citation() produces when a package has no citation file of its own. If it prints something else, use that.

Cite the R Observatory

For a number measured here: a download total, a coverage figure, an archival date.

APA

Balamuta, J. J. (2026). R Observatory: Metrics for PEPBVS version 2.2 [Data set]. HJJB, LLC. Data release v2026-08-15. https://doi.org/10.5281/zenodo.21843040

From data release v2026-08-15, which the citation names so these numbers can be found later. More on citing and the projects behind them.

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