Mhorseshoe package in R: Approximate algorithm for the horseshoe prior in Bayesian linear model

The horseshoe prior is a continuous shrinkage prior frequently used in high-dimensional Bayesian sparse linear regression models. Although the horseshoe prior theoretically guarantees excellent shrinkage properties, performing a Markov Chain Monte Carlo (MCMC) algorithm incurs high computational cos...

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Bibliographic Details
Published in:SoftwareX Vol. 31; p. 102236
Main Authors: Kang, Mingi, Lee, Kyoungjae
Format: Journal Article
Language:English
Published: Elsevier B.V 01.09.2025
Elsevier
Subjects:
Approximate algorithm
Bayesian inference
Horseshoe prior
R
Approximate algorithm
Bayesian inference
Horseshoe prior
ISSN:2352-7110, 2352-7110
Online Access:Get full text
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Summary:The horseshoe prior is a continuous shrinkage prior frequently used in high-dimensional Bayesian sparse linear regression models. Although the horseshoe prior theoretically guarantees excellent shrinkage properties, performing a Markov Chain Monte Carlo (MCMC) algorithm incurs high computational costs per iteration. We introduce the Mhorseshoe package in R, which implements posterior inference under the horseshoe prior, based on the exact and approximate algorithms proposed in Johndrow et al. (2020). Furthermore, this package incorporates a novel adaptive selection method, which we developed and implemented to determine the tuning parameter in the approximate algorithm. We conducted a simulation study and confirmed that the algorithm can be effectively applied to large datasets.
ISSN:2352-7110
2352-7110
DOI:10.1016/j.softx.2025.102236