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Adaptive penalization in high-dimensional regression and classification with external covariates using variational Bayes

Bioconductor version: Release (3.19)

This package enables regression and classification on high-dimensional data with different relative strengths of penalization for different feature groups, such as different assays or omic types. The optimal relative strengths are chosen adaptively. Optimisation is performed using a variational Bayes approach.

Author: Britta Velten [aut, cre], Wolfgang Huber [aut]

Maintainer: Britta Velten <britta.velten at>

Citation (from within R, enter citation("graper")):


To install this package, start R (version "4.4") and enter:

if (!require("BiocManager", quietly = TRUE))


For older versions of R, please refer to the appropriate Bioconductor release.


To view documentation for the version of this package installed in your system, start R and enter:

example_linear HTML R Script
example_logistic HTML R Script
Reference Manual PDF


biocViews Bayesian, Classification, Regression, Software
Version 1.20.0
In Bioconductor since BioC 3.9 (R-3.6) (5 years)
License GPL (>= 2)
Depends R (>= 3.6)
Imports Matrix, Rcpp, stats, ggplot2, methods, cowplot, matrixStats
System Requirements
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Suggests knitr, rmarkdown, BiocStyle, testthat
Linking To Rcpp, RcppArmadillo, BH
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Follow Installation instructions to use this package in your R session.

Source Package graper_1.20.0.tar.gz
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macOS Binary (arm64) graper_1.20.0.tgz
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