SeBR: Semiparametric Bayesian Regression Analysis

Monte Carlo and MCMC 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 (2023) <doi:10.48550/arXiv.2306.05498>.

Version: 1.0.0
Imports: fields, GpGp, MASS, quantreg, spikeSlabGAM, statmod
Suggests: knitr, rmarkdown
Published: 2023-07-03
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.pdf
Vignettes: Introduction to SeBR

Downloads:

Package source: SeBR_1.0.0.tar.gz
Windows binaries: r-devel: SeBR_1.0.0.zip, r-release: SeBR_1.0.0.zip, r-oldrel: SeBR_1.0.0.zip
macOS binaries: r-release (arm64): SeBR_1.0.0.tgz, r-oldrel (arm64): SeBR_1.0.0.tgz, r-release (x86_64): SeBR_1.0.0.tgz

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