Type: Package
Package: SeBR
Title: Semiparametric Bayesian Regression Analysis
Version: 1.1.0
Authors@R: 
    person("Dan", "Kowal", email = "daniel.r.kowal@gmail.com", role = c("aut", "cre", "cph"),
           comment = c(ORCID = "0000-0003-0917-3007"))
Description: 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>.
License: MIT + file LICENSE
URL: https://github.com/drkowal/SeBR, https://drkowal.github.io/SeBR/
BugReports: https://github.com/drkowal/SeBR/issues
Imports: graphics, stats
Suggests: fields, GpGp, knitr, MASS, plyr, quantreg, rmarkdown,
        spikeSlabGAM, statmod
VignetteBuilder: knitr
Encoding: UTF-8
RoxygenNote: 7.2.3
NeedsCompilation: no
Packaged: 2025-06-16 22:09:55 UTC; danielkowal
Author: Dan Kowal [aut, cre, cph] (ORCID:
    <https://orcid.org/0000-0003-0917-3007>)
Maintainer: Dan Kowal <daniel.r.kowal@gmail.com>
Repository: CRAN
Date/Publication: 2025-06-16 22:20:02 UTC
Built: R 4.3.3; ; 2025-06-19 03:45:18 UTC; unix
