Packaged: 2022-11-29 12:45:31 UTC; wittenburg

Title: Hierarchical Shrinkage Stan Models for Biomarker Selection
Description: Linear and logistic regression models penalized with hierarchical
shrinkage priors for selection of biomarkers (or more general variable
selection), which can be fitted using Stan (Carpenter et al. (2017)
<doi:10.18637/jss.v076.i01>). It implements the horseshoe and regularized
horseshoe priors (Piironen and Vehtari (2017) <doi:10.1214/17-EJS1337SI>),
as well as the projection predictive selection approach to recover a sparse
set of predictive biomarkers (Piironen, Paasiniemi and Vehtari (2018)
Imports: ggplot2, loo (>= 2.1.0), parallel, pROC, Rcpp, methods, rstan
Author: Marco Colombo [aut, cre] (<https://orcid.org/0000-0001-6672-0623>),

set of predictive biomarkers (Piironen, Paasiniemi and Vehtari (2020)
Imports: ggplot2, loo (>= 2.1.0), parallel, pROC, Rcpp, RcppParallel

Title: Horizontal Time Dependent Positioning
person("National Geodetic Survey", , , "ngs.cors.htdp@noaa.gov",
role = "cph", comment = "Author of included HTDP code")
Description: Provides bindings to the National Geodetic Survey (NGS) Horizontal
Time Dependent Positioning (HTDP) utility, v3.2.5, written by Richard Snay,
Chris Pearson, and Jarir Saleh of NGS. HTDP is a utility that allows users to
transform positional coordinates across time and between spatial reference
frames. See <https://www.ngs.noaa.gov/TOOLS/Htdp/Htdp.shtml> for more
Packaged: 2016-09-19 06:32:29 UTC; jbuonagurio
National Geodetic Survey [cph] (Author of included HTDP code)

Title: Reweighted Marginal Hypothesis Tests for Clustered Data
person(given = "Mary", family = "Gregg", role = c("aut", "cre"), email = "megregg07@gmail.com", comment = c(ORCID = "0000-0003-2991-6939")),
person(given = "Somnath", family = "Datta", role = "aut", comment = c(ORCID = "0000-0003-4381-1842")),
person(given = "Doug", family = "Lorenz", role = "aut", comment = c(ORCID = "0000-0001-8114-0926"))
Description: A collection of reweighted marginal hypothesis tests for clustered data, based
on reweighting methods of Williamson, J., Datta, S., and Satten, G. (2003) <doi:10.1111/1541-0420.00005>.
The tests in this collection are clustered analogs to well-known hypothesis tests
in the classical setting, and are appropriate for data with cluster- and/or group-size
informativeness. The syntax and output of functions are modeled after common,
recognizable functions native to R. Methods used in the package refer to
Nevalainen, J., Oja, H., and Datta, S. (2017) <doi:10.1002/sim.7288>
Author: Mary Gregg [aut, cre] (<https://orcid.org/0000-0003-2991-6939>),