BayesQRCount
0.1.0Adaptive Bayesian Quantile Regression for Count Data
Overview
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
- OK2026-08-066 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
Documentation
- Examples that run
- 76%
- Documented parameters
- 97%
- Return-value docs
- 100%
- References docs
- 0%
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Code & Tests
Datasets
People & History
1 release. R releases are shown for context.
- 0.1.0Latest2026-08-05 · current release
- RR 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
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