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BayesQRCount

0.1.0

Adaptive Bayesian Quantile Regression for Count Data

0packages depend
66downloads / year
75.9%test coverage
11/11checks pass

Overview

About
Maintained by Shikhar TyagiFirst published 2026-08-051 releasesCRAN page ↗

Implements Bayesian quantile regression for count data using the jittering technique for discrete data smoothing and an asymmetric Laplace distribution likelihood. Supports adaptive variable selection via a random-bridge penalty with a beta prior on the power parameter, as well as fixed-bridge and Lasso penalties. Utilizes Markov chain Monte Carlo with Gibbs sampling and adaptive Metropolis-Hastings algorithms for posterior inference, provides Gelman-Rubin convergence diagnostics, and predicts conditional quantiles for count responses. Methodology and applications are based on the following key references: Luo, Zhou, Hu, and Li (2026, Journal of Mathematics, 2026:1543166, doi:10.1155/jom/1543166), Koenker and Bassett (1978, Econometrica, 46, 33-50, doi:10.2307/1913643), Machado and Santos Silva (2005, Journal of the American Statistical Association, 100, 1226-1237, doi:10.1198/016214505000000330), Yu and Moyeed (2001, Statistics and Probability Letters, 54, 437-447, doi:10.1016/S0167-7152(01)00124-9), Polson, Scott, and Windle (2014, Journal of the Royal Statistical Society Series B, 76, 713-733, doi:10.1111/rssb.12042), Park and Casella (2008, Journal of the American Statistical Association, 103, 681-686, doi:10.1198/016214508000000337), and Roberts and Rosenthal (2009, Journal of Computational and Graphical Statistics, 18, 349-367, doi:10.1198/jcgs.2009.06134).

Install

Health

CRAN checks
11OK
Slowest check: 1.3 min · r-release-windows-x86_64
Code health
Yes
Tests · ratio 0.15
75.9%
Coverage · measured lines
100%
Documentation · exports
3
Dependencies · direct
Check history
  • OK2026-08-06
    6 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE

Documentation

Documentation
READMENoVignettesNopkgdown siteNoNEWSNoCode of conductNoContributing guideNo
Examples that run
76%
Documented parameters
97%
Return-value docs
100%
References docs
0%

Downloads

66
CRAN downloads in the past year
Rank #24,761 · ~0/day · ~6/mo
Daily download trend is not available in this view yet.
Also on1 autocran

Dependencies

Declared dependencies
1 external dependency (excludes base and recommended)
Depends (1)
R >= 4.0.0
Imports (3)
statsgraphicsgrDevices
LinkingTo (0)
none
Suggests (1)
Enhances (0)
none
Reverse dependencies
0direct
0indirect

Nothing depends on this yet.

Code & Tests

Datasets

People & History

People (4)
Maintainer (1)
Author, Maintainer
Authors (4)
Author, Maintainer
Package Timeline

1 release. R releases are shown for context.

  • 0.1.0Latest
    2026-08-05 · current release
  • R
    R 4.6.0 released · 2026-04-24

Package metadata

First published
2026-08-05
Total releases
1 / 1 yrs
License
GPL (>= 3) OSI
Minimum R
≥ 4.0.0
Bundled data
7.8 KB / 1 file
Download size
not tracked yet
Installed size
not tracked yet
With dependencies
not tracked yet

Cite

Cite this package

Run in R for the authors' preferred citation:

citation("BayesQRCount")
Tyagi, S., Pandey, A., Singh, B., & Tripathi, V. (2026). BayesQRCount: Adaptive Bayesian Quantile Regression for Count Data (Version 0.1.0) [Computer software]. https://doi.org/10.32614/CRAN.package.BayesQRCount

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 BayesQRCount version 0.1.0 [Data set]. HJJB, LLC. Data release v2026-08-11. https://doi.org/10.5281/zenodo.21843040

From data release v2026-08-11, which the citation names so these numbers can be found later. More on citing and the projects behind them.

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