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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 |
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 |
Reference manual: | SeBR.pdf |
Vignettes: |
Introduction to SeBR |
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, r-oldrel (x86_64): SeBR_1.0.0.tgz |
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.