horseshoe: Implementation of the Horseshoe Prior

Contains functions for applying the horseshoe prior to high- dimensional linear regression, yielding the posterior mean and credible intervals, amongst other things. The key parameter tau can be equipped with a prior or estimated via maximum marginal likelihood estimation (MMLE). The main function, horseshoe, is for linear regression. In addition, there are functions specifically for the sparse normal means problem, allowing for faster computation of for example the posterior mean and posterior variance. Finally, there is a function available to perform variable selection, using either a form of thresholding, or credible intervals.

AuthorStephanie van der Pas [cre, aut], James Scott [aut], Antik Chakraborty [aut], Anirban Bhattacharya [aut]
Date of publication2016-11-08 18:36:01
MaintainerStephanie van der Pas <svdpas@math.leidenuniv.nl>
LicenseGPL-3
Version0.1.0

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