Package: SeBR 1.1.0

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_1.1.0.tar.gz
SeBR_1.1.0.zip(r-4.7-any)SeBR_1.1.0.zip(r-4.6-any)SeBR_1.1.0.zip(r-4.5-any)
SeBR_1.1.0.tgz(r-4.6-any)SeBR_1.1.0.tgz(r-4.5-any)
SeBR_1.1.0.tar.gz(r-4.7-any)SeBR_1.1.0.tar.gz(r-4.6-any)
SeBR_1.1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
SeBR/json (API)

# 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/docs site:https://drkowal.github.io

On CRAN:

4.18 score 3 stars 233 downloads 22 exports 0 dependencies

Last updated from:83375b7961. Checks:9 OK. Indexed: yes.

Target	Result	Time
linux-devel	OK	163
source / vignettes	OK	236
linux-release	OK	132
macos-release	OK	165
macos-oldrel	OK	194
windows-devel	OK	126
windows-release	OK	110
windows-oldrel	OK	98
wasm-release	OK	115

Exports:bb bgp_bc blm_bc blm_bc_hs bqr bsm_bc concen_hbb g_bc g_inv_bc hbb plot_pptest rank_approx sampleFastGaussian sbgp sblm sblm_hs sblm_modelsel sblm_ssvs sbqr sbsm simulate_tlm sir_adjust

Dependencies:

Introduction to SeBR

Rendered fromSeBR.Rmdusingknitr::rmarkdown

Last update: 2026-03-03
Started: 2023-06-21

Help page	Topics
Compute all subsets of a set	all_subsets
Bayesian bootstrap posterior sampler for the CDF	bb
Bayesian Gaussian processes with a Box-Cox transformation	bgp_bc
Bayesian linear model with a Box-Cox transformation	blm_bc
Bayesian linear model with a Box-Cox transformation and a horseshoe prior	blm_bc_hs
Bayesian quantile regression	bqr
Bayesian spline model with a Box-Cox transformation	bsm_bc
Estimate the remaining time in the algorithm	computeTimeRemaining
Posterior sampling algorithm for the HBB concentration hyperparameters	concen_hbb
Grid contraction	contract_grid
Compute the latent data CDF	Fz_fun
Box-Cox transformation	g_bc
Compute the transformation	g_fun
Approximate inverse transformation	g_inv_approx
Inverse Box-Cox transformation	g_inv_bc
Hierarchical Bayesian bootstrap posterior sampler	hbb
Plot point and interval predictions on testing data	plot_pptest
Rank-based estimation of the linear regression coefficients	rank_approx
Sample a Gaussian vector using Bhattacharya et al. (2016)	sampleFastGaussian
Semiparametric Bayesian Gaussian processes	sbgp
Semiparametric Bayesian linear model	sblm
Semiparametric Bayesian linear model with horseshoe priors for high-dimensional data	sblm_hs
Model selection for semiparametric Bayesian linear regression	sblm_modelsel
Semiparametric Bayesian linear model with stochastic search variable selection	sblm_ssvs
Semiparametric Bayesian quantile regression	sbqr
Semiparametric Bayesian spline model	sbsm
Simulate a transformed linear model	simulate_tlm
Post-processing with importance sampling	sir_adjust
Numerically stabilize the squared elements	square_stabilize
Compute the sum-squared-residuals term under Zellner's g-prior	SSR_gprior
Univariate Slice Sampler from Neal (2008)	uni.slice

Package: SeBR 1.1.0

SeBR: Semiparametric Bayesian Regression Analysis

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