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.

Getting started

Package details

AuthorHamed Haselimashhadi <>
MaintainerHamed Haselimashhadi <>
LicenseLGPL (>= 2)
Package repositoryView on CRAN
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BDWreg documentation built on May 2, 2019, 9:31 a.m.