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horseshoe  

Implementation of the Horseshoe Prior
View on CRAN: Click here


Download and install horseshoe package within the R console
Install from CRAN:
install.packages("horseshoe")

Install from Github:
library("remotes")
install_github("cran/horseshoe")

Install by package version:
library("remotes")
install_version("horseshoe", "0.2.0")



Attach the package and use:
library("horseshoe")
Maintained by
Stephanie van der Pas
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2016-11-08
Latest Update: 2019-07-18
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.
How to cite:
Stephanie van der Pas (2016). horseshoe: Implementation of the Horseshoe Prior. R package version 0.2.0, https://cran.r-project.org/web/packages/horseshoe. Accessed 21 Nov. 2024.
Previous versions and publish date:
0.1.0 (2016-11-08 18:36)
Other packages that cited horseshoe R package
View horseshoe citation profile
Other R packages that horseshoe depends, imports, suggests or enhances
Complete documentation for horseshoe
Functions, R codes and Examples using the horseshoe R package
Some associated functions: Basic.integrand . Basic.y . Basic.y.vec . HS.MMLE . HS.normal.means . HS.post.mean . HS.post.var . HS.var.select . horseshoe . 
Some associated R codes: HS_MMLE.R . HS_normal_means.R . HS_post_mean.R . HS_post_var.R . HS_var_select.R . Helper_functions.R . horseshoe.R .  Full horseshoe package functions and examples
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