recipe bioconductor-graper

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



GPL (>= 2)



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.

package bioconductor-graper

(downloads) docker_bioconductor-graper



depends libblas:


depends libgcc-ng:


depends liblapack:


depends libstdcxx-ng:


depends r-base:


depends r-bh:

depends r-cowplot:

depends r-ggplot2:

depends r-matrix:

depends r-matrixstats:

depends r-rcpp:

depends r-rcpparmadillo:



You need a conda-compatible package manager (currently either micromamba, mamba, or conda) and the Bioconda channel already activated (see set-up-channels).

While any of above package managers is fine, it is currently recommended to use either micromamba or mamba (see here for installation instructions). We will show all commands using mamba below, but the arguments are the same for the two others.

Given that you already have a conda environment in which you want to have this package, install with:

   mamba install bioconductor-graper

and update with::

   mamba update bioconductor-graper

To create a new environment, run:

mamba create --name myenvname bioconductor-graper

with myenvname being a reasonable name for the environment (see e.g. the mamba docs for details and further options).

Alternatively, use the docker container:

   docker pull<tag>

(see `bioconductor-graper/tags`_ for valid values for ``<tag>``)

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