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horseshoe (version 0.2.0)

Implementation of the Horseshoe Prior

Description

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.

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Version

Install

install.packages('horseshoe')

Monthly Downloads

36

Version

0.2.0

License

GPL-3

Maintainer

Stephanie van der Pas

Last Published

July 18th, 2019

Functions in horseshoe (0.2.0)

Basic.integrand

Helper function for computing the posterior mean, posterior variance
HS.post.mean

Posterior mean for the horseshoe for the normal means problem.
HS.normal.means

The horseshoe prior for the sparse normal means problem
HS.post.var

Posterior variance for the horseshoe for the normal means problem.
HS.var.select

Variable selection using the horseshoe prior
HS.MMLE

MMLE for the horseshoe prior for the sparse normal means problem.
horseshoe

Function to implement the horseshoe shrinkage prior in Bayesian linear regression
Basic.y.vec

Helper function for computing the posterior mean, posterior variance
Basic.y

Helper function for computing the posterior mean, posterior variance