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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 |
| Reference manual: | BDWreg.pdf |
| 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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