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.
|Depends:||R (≥ 3.0)|
|Imports:||coda, parallel, foreach, doParallel, MASS, methods, graphics, stats, utils, DWreg|
|Maintainer:||Hamed Haselimashhadi <hamedhaseli at gmail.com>|
|License:||LGPL-2 | LGPL-2.1 | LGPL-3 [expanded from: LGPL (≥ 2)]|
|CRAN checks:||BDWreg results|
|Windows binaries:||r-devel: BDWreg_1.2.0.zip, r-release: BDWreg_1.2.0.zip, r-oldrel: BDWreg_1.2.0.zip|
|OS X Mavericks binaries:||r-release: BDWreg_1.2.0.tgz, r-oldrel: BDWreg_1.2.0.tgz|
|Old sources:||BDWreg archive|
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