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SeBR: Semiparametric Bayesian Regression Analysis
Monte Carlo sampling algorithms for semiparametric Bayesian regression analysis. These models feature a nonparametric (unknown) transformation of the data paired with widely-used regression models including linear regression, spline regression, quantile regression, and Gaussian processes. The transformation enables broader applicability of these key models, including for real-valued, positive, and compactly-supported data with challenging distributional features. The samplers prioritize computational scalability and, for most cases, Monte Carlo (not MCMC) sampling for greater efficiency. Details of the methods and algorithms are provided in Kowal and Wu (2024) <doi:10.1080/01621459.2024.2395586>.
Getting started
Browse package contents
Package details |
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| Author | Dan Kowal [aut, cre, cph] (ORCID: <https://orcid.org/0000-0003-0917-3007>) |
| Maintainer | Dan Kowal <daniel.r.kowal@gmail.com> |
| License | MIT + file LICENSE |
| Version | 1.1.0 |
| URL | https://github.com/drkowal/SeBR https://drkowal.github.io/SeBR/ |
| Package repository | View on CRAN |
| Installation |
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