recipe r-gkmsvm

Imports the 'gkmSVM' v2.0 functionalities into R <http://www.beerlab.org/gkmsvm/> It also uses the 'kernlab' library (separate R package by different authors) for various SVM algorithms.

Homepage:

https://CRAN.R-project.org/package=gkmSVM

License:

GPL2 / GPL-2.0-or-later

Recipe:

/r-gkmsvm/meta.yaml

package r-gkmsvm

(downloads) docker_r-gkmsvm

versions:
0.83.0-00.82.0-30.82.0-20.82.0-10.82.0-00.81.0-40.81.0-30.81.0-20.81.0-1

0.83.0-00.82.0-30.82.0-20.82.0-10.82.0-00.81.0-40.81.0-30.81.0-20.81.0-10.81.0-00.80.0-10.80.0-00.79.0-10.79.0-00.71.0-0

depends bioconductor-biocgenerics:

depends bioconductor-biostrings:

depends bioconductor-genomeinfodb:

depends bioconductor-genomicranges:

depends bioconductor-iranges:

depends bioconductor-rtracklayer:

depends bioconductor-s4vectors:

depends libgcc-ng:

>=12

depends libstdcxx-ng:

>=12

depends r-base:

>=4.3,<4.4.0a0

depends r-kernlab:

depends r-rcpp:

depends r-rocr:

depends r-seqinr:

requirements:

Installation

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 r-gkmsvm

and update with::

   mamba update r-gkmsvm

To create a new environment, run:

mamba create --name myenvname r-gkmsvm

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 quay.io/biocontainers/r-gkmsvm:<tag>

(see `r-gkmsvm/tags`_ for valid values for ``<tag>``)

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