BDWreg: Bayesian Inference for Discrete Weibull Regression

A Bayesian regression model for discrete response, where the conditional distribution is modelled via a discrete Weibull distribution. This package provides an implementation of Metropolis-Hastings and Reversible-Jumps algorithms to draw samples from the posterior. It covers a wide range of regularizations through any two parameter prior. Examples are Laplace (Lasso), Gaussian (ridge), Uniform, Cauchy and customized priors like a mixture of priors. An extensive visual toolbox is included to check the validity of the results as well as several measures of goodness-of-fit.

Version: 1.3.0
Depends: R (≥ 3.0)
Imports: coda, parallel, foreach, doParallel, MASS, methods, graphics, stats, utils, DWreg
Published: 2024-01-29
DOI: 10.32614/CRAN.package.BDWreg
Author: Hamed Haselimashhadi
Maintainer: Hamed Haselimashhadi <hamedhaseli at>
License: LGPL-2 | LGPL-2.1 | LGPL-3 [expanded from: LGPL (≥ 2)]
NeedsCompilation: no
CRAN checks: BDWreg results


Reference manual: BDWreg.pdf


Package source: BDWreg_1.3.0.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): BDWreg_1.3.0.tgz, r-oldrel (arm64): BDWreg_1.3.0.tgz, r-release (x86_64): BDWreg_1.3.0.tgz, r-oldrel (x86_64): BDWreg_1.3.0.tgz
Old sources: BDWreg archive


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