Package: horseshoe
Title: Implementation of the Horseshoe Prior
Version: 0.2.0
Authors@R: c(
person("Stephanie", "van der Pas", email = "svdpas@math.leidenuniv.nl", role = c("cre", "aut")),
person("James", "Scott", email = "James.Scott@mccombs.utexas.edu", role = "aut"),
person("Antik", "Chakraborty", email = "antik@stat.tamu.edu", role = "aut"),
person("Anirban", "Bhattacharya", email = "anirbanb@stat.tamu.edu", role = "aut"))
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.
Depends: R (>= 3.1.0)
Imports: stats
Suggests: Hmisc, ggplot2, knitr, rmarkdown
Encoding: UTF-8
License: GPL-3
LazyData: false
RoxygenNote: 6.1.1
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2019-07-18 09:48:43 UTC; stephanie
Author: Stephanie van der Pas [cre, aut],
James Scott [aut],
Antik Chakraborty [aut],
Anirban Bhattacharya [aut]
Maintainer: Stephanie van der Pas <svdpas@math.leidenuniv.nl>
Repository: CRAN
Date/Publication: 2019-07-18 10:14:05 UTC

Title: Nonlinear Regression using Horseshoe Prior
c(person("Arnab Kumar", "Maity", email = "Arnab.Maity@pfizer.com", role = c("aut", "cre")))
Description: Provides the posterior estimates of the regression coefficients when horseshoe prior is specified.
The regression models considered here are logistic model for binary response and
log normal accelerated failure time model for right censored survival response.
The linear model analysis is also available for completeness.
All models provide deviance information criterion and widely applicable information criterion.
Maintainer: Arnab Kumar Maity <Arnab.Maity@pfizer.com>