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.
|Author||Hamed Haselimashhadi <email@example.com>|
|Date of publication||2017-02-17 14:37:54|
|Maintainer||Hamed Haselimashhadi <firstname.lastname@example.org>|
|License||LGPL (>= 2)|
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