Package: SeBR 1.0.0

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>.

Authors:Dan Kowal [aut, cre, cph]

SeBR_1.0.0.tar.gz
SeBR_1.0.0.zip(r-4.5)SeBR_1.0.0.zip(r-4.4)SeBR_1.0.0.zip(r-4.3)
SeBR_1.0.0.tgz(r-4.5-any)SeBR_1.0.0.tgz(r-4.4-any)SeBR_1.0.0.tgz(r-4.3-any)
SeBR_1.0.0.tar.gz(r-4.5-noble)SeBR_1.0.0.tar.gz(r-4.4-noble)
SeBR_1.0.0.tgz(r-4.4-emscripten)SeBR_1.0.0.tgz(r-4.3-emscripten)
SeBR.pdf |SeBR.html✨
SeBR/json (API)
NEWS

# Install 'SeBR' in R:
install.packages('SeBR', repos = c('https://drkowal.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/drkowal/sebr/issues

Pkgdown site:https://drkowal.github.io

On CRAN:

Conda:

4.30 score 1 stars 3 scripts 154 downloads 17 exports 0 dependencies

Last updated 17 days agofrom:44b6656864. Checks:9 OK. Indexed: yes.

TargetResultLatest binary
Doc / VignettesOKMar 10 2025
R-4.5-winOKMar 10 2025
R-4.5-macOKMar 10 2025
R-4.5-linuxOKMar 10 2025
R-4.4-winOKMar 10 2025
R-4.4-macOKMar 10 2025
R-4.4-linuxOKMar 10 2025
R-4.3-winOKMar 10 2025
R-4.3-macOKMar 10 2025

Exports:bbbgp_bcblm_bcbqrbsm_bcconcen_hbbg_bcg_inv_bchbbplot_pptestrank_approxsbgpsblmsbqrsbsmsimulate_tlmsir_adjust

Dependencies:

Introduction to SeBR

Rendered fromSeBR.Rmdusingknitr::rmarkdownon Mar 10 2025.

Last update: 2024-09-03
Started: 2023-06-21