BDWreg
1.3.0Bayesian Inference for Discrete Weibull Regression
Overview
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.
Install
Health
- NOTE r-devel-linux-x86_64-debian-clang
- NOTE r-devel-linux-x86_64-debian-gcc
- NOTE2026-03-1012 OK · 2 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
Documentation
- Examples that run
- 75%
- Documented parameters
- 98%
- Return-value docs
- 100%
- References docs
- 100%
Downloads
Dependencies
Nothing depends on this yet.
Code & Tests
People & History
Author records are not tracked yet for this package.
3 releases. Pick two to compare their code metrics. R releases are shown for context.
- RR 4.6.0 released · 2026-04-24
- RR 4.5.0 released · 2025-04-11
- RR 4.4.0 released · 2024-04-24
- 1.3.0Latest
- RR 4.3.0 released · 2023-04-21
- RR 4.2.0 released · 2022-04-22
- RR 4.1.0 released · 2021-05-18
- RR 4.0.0 released · 2020-04-24
- RR 3.6.0 released · 2019-04-26
- RR 3.5.0 released · 2018-04-23
- RR 3.4.0 released · 2017-04-21
- 1.2.02017-02-17 · diff ↗
- RR 3.3.0 released · 2016-05-03
- 1.0.02016-01-12
- RR 3.2.0 released · 2015-04-16
Package metadata
- First published
- 2016-01-12
- Total releases
- 3 / 10 yrs
- License
- LGPL (>= 2) OSI
- Minimum R
- ≥ 3.0
- Download size
- 20 KB
- Installed size
- not tracked yet
- With dependencies
- not tracked yet
Cite
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