BDWreg: Bayesian Inference for Discrete Weibull Regression
Version 1.2.0

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 <>
Date of publication2017-02-17 14:37:54
MaintainerHamed Haselimashhadi <>
LicenseLGPL (>= 2)
Package repositoryView on CRAN
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BDWreg documentation built on May 30, 2017, 12:31 a.m.