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 |