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Brq: an R package for Bayesian quantile regression

Author

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  • Rahim Alhamzawi

    (University of Al-Qadisiyah)

  • Haithem Taha Mohammad Ali

    (Nawroz University)

Abstract

Bayesian regression quantile has received much attention in recent literature. The objective of this paper is to illustrate Brq, a new software package in R. Brq allows for the Bayesian coefficient estimation and variable selection in regression quantile (RQ) and support Tobit and binary RQ. In addition, this package implements the Bayesian Tobit and binary RQ with lasso and adaptive lasso penalties. Further modeling functions for summarising the results, drawing trace plots, posterior histograms, autocorrelation plots, and plotting quantiles are included.

Suggested Citation

  • Rahim Alhamzawi & Haithem Taha Mohammad Ali, 2020. "Brq: an R package for Bayesian quantile regression," METRON, Springer;Sapienza Università di Roma, vol. 78(3), pages 313-328, December.
    • Handle: RePEc:spr:metron:v:78:y:2020:i:3:d:10.1007_s40300-020-00190-6
    DOI: 10.1007/s40300-020-00190-6
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    References listed on IDEAS

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    3. Magzhanov, Timur & Sagradyan, Anna, 2023. "Ambiguous high scores: The All-Russian Olympiad in economics during the COVID-19 pandemic," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 70, pages 89-108.

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