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hsstan
View on CRAN: Click
here
Download and install hsstan package within the R console
Install from CRAN:
install.packages("hsstan")
Install from Github:
library("remotes")
install_github("cran/hsstan") Install by package version:
library("remotes")
install_version("hsstan", "0.8.2") Attach the package and use:
library("hsstan")
Maintained by
Marco Colombo
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2019-09-22
Latest Update: 2024-01-13
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)
). It implements the horseshoe and regularized
horseshoe priors (Piironen and Vehtari (2017) ),
as well as the projection predictive selection approach to recover a sparse
set of predictive biomarkers (Piironen, Paasiniemi and Vehtari (2020)
).
How to cite:
Marco Colombo (2019). hsstan: Hierarchical Shrinkage Stan Models for Biomarker Selection. R package version 0.8.2, https://cran.r-project.org/web/packages/hsstan. Accessed 26 Oct. 2026.
Previous versions and publish date:
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imports, suggests or enhances
Complete documentation for hsstan
Functions, R codes and Examples using
the hsstan R package
Some associated functions: bayes_R2.hsstan . choose.next . diabetes . fit.submodel . hsstan-package . hsstan . kfold.hsstan . lm_proj . log_lik.hsstan . loo.hsstan . nsamples.hsstan . plot.projsel . posterior_interval.hsstan . posterior_linpred.hsstan . posterior_performance . posterior_predict.hsstan . posterior_summary . print.hsstan . projsel . sampler.stats . summary.hsstan .
Some associated R codes: diabetes.R . hsstan-package.R . misc.R . postestimation.R . projection.R . stan.R . stanmodels.R . summaries.R . Full hsstan package functions and examples
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