hsstan: Hierarchical Shrinkage Stan Models for Biomarker Selection

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 (2020) <doi:10.1214/20-EJS1711>).

Version:	0.8
Depends:	R (≥ 3.6)
Imports:	ggplot2, loo (≥ 2.1.0), parallel, pROC, Rcpp, methods, rstan (≥ 2.18.1), rstantools (≥ 2.0.0), stats, utils
LinkingTo:	BH (≥ 1.66.0.1), Rcpp (≥ 0.12.15), RcppEigen (≥ 0.3.3.4.0), StanHeaders (≥ 2.17.2), rstan (≥ 2.18.1)
Suggests:	testthat (≥ 2.1.0)
Published:	2020-06-29
Author:	Marco Colombo [aut, cre], Paul McKeigue [aut], Athina Spiliopoulou [ctb]
Maintainer:	Marco Colombo <mar.colombo13 at gmail.com>
BugReports:	https://github.com/mcol/hsstan/issues
License:	GPL-3
URL:	https://github.com/mcol/hsstan
NeedsCompilation:	yes
SystemRequirements:	GNU make
Materials:	README NEWS
CRAN checks:	hsstan results

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