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) <arXiv:1810.02406>).
|Author||Marco Colombo [aut, cre] (<https://orcid.org/0000-0001-6672-0623>), Paul McKeigue [aut] (<https://orcid.org/0000-0002-5217-1034>), Athina Spiliopoulou [ctb] (<https://orcid.org/0000-0002-5929-6585>)|
|Maintainer||Marco Colombo <[email protected]>|
|Package repository||View on CRAN|
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