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: 0.8.2 Depends: R (≥ 3.6) Imports: ggplot2, loo (≥ 2.1.0), parallel, pROC, Rcpp, methods, rstan (≥ 2.26.0), rstantools (≥ 2.0.0), stats, utils LinkingTo: BH (≥ 1.66.0.1), Rcpp (≥ 0.12.15), RcppEigen (≥ 0.3.3.4.0), RcppParallel (≥ 5.0.1), StanHeaders (≥ 2.26.0), rstan (≥ 2.26.0) Suggests: testthat (≥ 2.1.0) Published: 2024-01-13 DOI: 10.32614/CRAN.package.hsstan 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 In views: Omics CRAN checks: hsstan results Documentation: Downloads: Reverse dependencies: Linking:Please use the canonical form https://CRAN.R-project.org/package=hsstan to link to this page.
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