recipe fastspar

Rapid and scalable correlation estimation for compositional data

Homepage:

https://github.com/scwatts/fastspar

License:

GPLv3

Recipe:

/fastspar/meta.yaml

Links:

doi: 10.1093/bioinformatics/bty734, doi: 10.1371/journal.pcbi.1002687

FastSpar is a C++ implementation of the SparCC algorithm which is up to several thousand times faster than the original Python2 release and uses much less memory. The FastSpar implementation provides threading support and a p-value estimator which accounts for the possibility of repetitious data permutations.

package fastspar

(downloads) docker_fastspar

versions:

1.0.0-5,  1.0.0-4,  1.0.0-3,  1.0.0-2,  1.0.0-1,  1.0.0-0,  0.0.10-0,  0.0.9-0,  0.0.6-0

depends armadillo:

>=12.2,<13.0a0

depends armadillo:

>=7.800.1

depends gsl:

>=2.7,<2.8.0a0

depends libgcc-ng:

>=12

depends libgfortran-ng:

depends libgfortran5:

>=12.2.0

depends libstdcxx-ng:

>=12

depends openblas:

* *openmp*

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 fastspar

and update with::

   mamba update fastspar

To create a new environment, run:

mamba create --name myenvname fastspar

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/fastspar:<tag>

(see `fastspar/tags`_ for valid values for ``<tag>``)

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