recipe bioconductor-rcm

Fit row-column association models with the negative binomial distribution for the microbiome






Combine ideas of log-linear analysis of contingency table, flexible response function estimation and empirical Bayes dispersion estimation for explorative visualization of microbiome datasets. The package includes unconstrained as well as constrained analysis. In addition, diagnostic plot to detect lack of fit are available.

package bioconductor-rcm

(downloads) docker_bioconductor-rcm



depends bioconductor-edger:


depends bioconductor-phyloseq:


depends r-alabama:

depends r-base:


depends r-dbi:

depends r-ggplot2:


depends r-mass:

depends r-nleqslv:

depends r-rcolorbrewer:

depends r-reshape2:

depends r-tensor:

depends r-tseries:

depends r-vgam:



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-rcm

and update with::

   mamba update bioconductor-rcm

To create a new environment, run:

mamba create --name myenvname bioconductor-rcm

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

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