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.
Package details |
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Author | Hamed Haselimashhadi <hamedhaseli@gmail.com> |
Maintainer | Hamed Haselimashhadi <hamedhaseli@gmail.com> |
License | LGPL (>= 2) |
Version | 1.2.0 |
URL | http://hamedhaseli.webs.com |
Package repository | View on CRAN |
Installation |
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