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 gmail.com>
License:	LGPL-2 | LGPL-2.1 | LGPL-3 [expanded from: LGPL (≥ 2)]
NeedsCompilation:	no
CRAN checks:	BDWreg results

Documentation:

Reference manual:

BDWreg.pdf

Downloads:

Package source:	BDWreg_1.3.0.tar.gz
Windows binaries:	r-devel: BDWreg_1.3.0.zip, r-release: BDWreg_1.3.0.zip, r-oldrel: BDWreg_1.3.0.zip
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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