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 |
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:
Downloads:
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