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pqrBayes

1.2.2

Bayesian Penalized Quantile Regression

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Overview

About
Maintained by Cen WuFirst published 2023-09-1412 releasesCRAN page ↗GitHub ↗

Bayesian regularized quantile regression utilizing two major classes of shrinkage priors (the spike-and-slab priors and the horseshoe family of priors) leads to efficient Bayesian shrinkage estimation, variable selection and valid statistical inference. In this package, we have implemented robust Bayesian variable selection with spike-and-slab priors under high-dimensional linear regression models (Fan et al. (2024) doi:10.3390/e26090794 and Ren et al. (2023) doi:10.1111/biom.13670), and regularized quantile varying coefficient models (Zhou et al.(2023) doi:10.1016/j.csda.2023.107808). In particular, valid robust Bayesian inferences under both models in the presence of heavy-tailed errors can be validated on finite samples. Additional models with spike-and-slab priors include robust Bayesian group LASSO and robust binary Bayesian LASSO (Fan and Wu (2025) doi:10.1002/sta4.70078). Besides, robust sparse Bayesian regression with the horseshoe family of (horseshoe, horseshoe+ and regularized horseshoe) priors has also been implemented and yielded valid inference results under heavy-tailed model errors (Fan et al.(2026) doi:10.1016/j.csda.2026.108358). The Markov chain Monte Carlo (MCMC) algorithms of the proposed and alternative models are implemented in C++.

Install

Health

CRAN checks
13OK
Slowest check: 7.2 min · r-devel-linux-x86_64-fedora-clang
Code health
None
Tests · ratio 0.00
not tracked
Coverage
100%
Documentation · exports
4
Dependencies · direct
Check history
  • OK2026-06-08
    13 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
  • NOTE2026-06-07
    12 OK · 1 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
  • OK2026-04-25
    12 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
  • NOTE2026-04-22
    12 OK · 2 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
  • ERROR2026-04-18
    11 OK · 2 NOTE · 0 WARNING · 1 ERROR · 0 FAILURE
Show 1 earlier snapshots
  • NOTE2026-03-10
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Documentation

Documentation
READMEYes · 2,389 wordsVignettesNopkgdown siteNoNEWSYes · 100% structuredCode of conductNoContributing guideNo
Examples that run
100%
Documented parameters
100%
Return-value docs
100%
References docs
30%

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Repository

Repository
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40Commits
1Contributors
40 commits · Last activity 2026-04-30 · 0% stars, 30d

Stars over time

2023-12-26 · 32026-07-07 · 4

Repository practices

Upstream repositoryBeta

1 development-tooling and community-health practice detected across 1 family in the upstream repository

Checks run against github.com/cenwu/pqrbayes on 2026-07-30.

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RStudio project
How this is detected·Detection ruleset v1 (2026-07-18)

Dependencies

Declared dependencies
4 external dependencies (excludes base and recommended)
Depends (1)
R >= 3.5.0
Imports (4)
Rcppglmnetsplinesstats
LinkingTo (2)
Suggests (0)
none
Enhances (0)
none
Reverse dependencies
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0indirect

Nothing depends on this yet.

Code & Tests

People & History

People (5)
Maintainer (1)
Author, Maintainer
Authors (5)
Author, Maintainer
Author · added in 1.0.3
Author · added in 1.1.2
Author
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Package Timeline

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

  • 1.2.2Latest
    2026-04-30 · current release · diff ↗
  • R
    R 4.6.0 released · 2026-04-24
  • 1.2.1
    2026-03-15 · diff ↗
  • 1.2.0
    2025-12-08 · diff ↗
  • 1.1.4
    2025-07-26 · diff ↗
  • 1.1.3
    2025-05-15 · diff ↗
  • R
    R 4.5.0 released · 2025-04-11
  • 1.1.2
    2025-04-01 · diff ↗
  • 1.1.1
    2025-02-24 · diff ↗
  • 1.1.0
    2025-02-17 · diff ↗
  • 1.0.5
    2025-02-05 · diff ↗
  • 1.0.4
    2025-01-25 · diff ↗
  • 1.0.3
    2024-12-21 · diff ↗
  • R
    R 4.4.0 released · 2024-04-24
  • 1.0.2
    2023-09-14
  • R
    R 4.3.0 released · 2023-04-21

Package metadata

First published
2023-09-14
Total releases
12 / 3 yrs
License
GPL-2 OSI
Minimum R
≥ 3.5.0
Bundled data
16 KB / 1 file
Download size
56 KB
Installed size
not tracked yet
With dependencies
not tracked yet
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