recipe bioconductor-dirichletmultinomial

Dirichlet-Multinomial Mixture Model Machine Learning for Microbiome Data







biotools: dirichletmultinomial, doi: 10.1371/journal.pone.0030126

Dirichlet-multinomial mixture models can be used to describe variability in microbial metagenomic data. This package is an interface to code originally made available by Holmes, Harris, and Quince, 2012, PLoS ONE 7(2): 1-15, as discussed further in the man page for this package, ?DirichletMultinomial.

package bioconductor-dirichletmultinomial

(downloads) docker_bioconductor-dirichletmultinomial



depends bioconductor-biocgenerics:


depends bioconductor-biocgenerics:


depends bioconductor-iranges:


depends bioconductor-iranges:


depends bioconductor-s4vectors:


depends bioconductor-s4vectors:


depends gsl:


depends libblas:


depends libgcc-ng:


depends liblapack:


depends r-base:




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-dirichletmultinomial

and update with::

   mamba update bioconductor-dirichletmultinomial

To create a new environment, run:

mamba create --name myenvname bioconductor-dirichletmultinomial

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-dirichletmultinomial/tags`_ for valid values for ``<tag>``)

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