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SeBR  

Semiparametric Bayesian Regression Analysis
View on CRAN: Click here


Download and install SeBR package within the R console
Install from CRAN:
install.packages("SeBR")

Install from Github:
library("remotes")
install_github("cran/SeBR")

Install by package version:
library("remotes")
install_version("SeBR", "1.0.0")



Attach the package and use:
library("SeBR")
Maintained by
Dan Kowal
[Scholar Profile | Author Map]
First Published: 2023-07-03
Latest Update: 2023-07-03
Description:
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) .
How to cite:
Dan Kowal (2023). SeBR: Semiparametric Bayesian Regression Analysis. R package version 1.0.0, https://cran.r-project.org/web/packages/SeBR. Accessed 17 Feb. 2025.
Previous versions and publish date:
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Complete documentation for SeBR
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Full SeBR package functions and examples
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