UBeRr provides Bayesian regularization methods for high-dimensional linear regression. The methods implemented in this package leverage a novel data augmentation scheme based on the scale mixture of uniform (SMU) distribution that leads to a set of efficient Gibbs samplers with tractable full conditional posterior distributions and superior performance over existing methods.
|Author||Himel Mallick <[email protected]>|
|Maintainer||Himel Mallick <[email protected]>|
|Package repository||View on GitHub|
Install the latest version of this package by entering the following in R:
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.