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>.
| Version: |
1.1.0 |
| Imports: |
graphics, stats |
| Suggests: |
fields, GpGp, knitr, MASS, plyr, quantreg, rmarkdown, spikeSlabGAM, statmod |
| Published: |
2025-06-16 |
| DOI: |
10.32614/CRAN.package.SeBR |
| Author: |
Dan Kowal  [aut,
cre, cph] |
| Maintainer: |
Dan Kowal <daniel.r.kowal at gmail.com> |
| BugReports: |
https://github.com/drkowal/SeBR/issues |
| License: |
MIT + file LICENSE |
| URL: |
https://github.com/drkowal/SeBR, https://drkowal.github.io/SeBR/ |
| NeedsCompilation: |
no |
| Materials: |
README, NEWS |
| CRAN checks: |
SeBR results |
Documentation:
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