recipe bioconductor-banocc

Bayesian ANalysis Of Compositional Covariance

Homepage:

https://bioconductor.org/packages/3.18/bioc/html/banocc.html

License:

MIT + file LICENSE

Recipe:

/bioconductor-banocc/meta.yaml

BAnOCC is a package designed for compositional data, where each sample sums to one. It infers the approximate covariance of the unconstrained data using a Bayesian model coded with `rstan`. It provides as output the `stanfit` object as well as posterior median and credible interval estimates for each correlation element.

package bioconductor-banocc

(downloads) docker_bioconductor-banocc

versions:
1.26.0-01.24.0-01.22.0-01.18.0-01.16.0-01.14.0-11.14.0-01.12.0-01.10.0-0

1.26.0-01.24.0-01.22.0-01.18.0-01.16.0-01.14.0-11.14.0-01.12.0-01.10.0-01.8.0-11.8.0-01.6.1-01.4.0-01.2.0-01.0.0-0

depends r-base:

>=4.3,<4.4.0a0

depends r-coda:

>=0.18.1

depends r-mvtnorm:

depends r-rstan:

>=2.17.4

depends r-stringr:

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 bioconductor-banocc

and update with::

   mamba update bioconductor-banocc

To create a new environment, run:

mamba create --name myenvname bioconductor-banocc

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/bioconductor-banocc:<tag>

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

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