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 ORCID iD [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:

Reference manual: SeBR.html , SeBR.pdf
Vignettes: Introduction to SeBR (source, R code)

Downloads:

Package source: SeBR_1.1.0.tar.gz
Windows binaries: r-devel: SeBR_1.1.0.zip, r-release: SeBR_1.1.0.zip, r-oldrel: SeBR_1.1.0.zip
macOS binaries: r-release (arm64): SeBR_1.1.0.tgz, r-oldrel (arm64): SeBR_1.1.0.tgz, r-release (x86_64): SeBR_1.1.0.tgz, r-oldrel (x86_64): SeBR_1.1.0.tgz
Old sources: SeBR archive

Linking:

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