recipe bioconductor-transformgampoi

Variance Stabilizing Transformation for Gamma-Poisson Models






Variance-stabilizing transformations help with the analysis of heteroskedastic data (i.e., data where the variance is not constant, like count data). This package provide two types of variance stabilizing transformations: (1) methods based on the delta method (e.g., 'acosh', 'log(x+1)'), (2) model residual based (Pearson and randomized quantile residuals).

package bioconductor-transformgampoi

(downloads) docker_bioconductor-transformgampoi



depends bioconductor-delayedarray:


depends bioconductor-glmgampoi:


depends bioconductor-hdf5array:


depends bioconductor-matrixgenerics:


depends bioconductor-summarizedexperiment:


depends libblas:


depends libgcc-ng:


depends liblapack:


depends libstdcxx-ng:


depends r-base:


depends r-matrix:

depends r-rcpp:



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-transformgampoi

and update with::

   mamba update bioconductor-transformgampoi

To create a new environment, run:

mamba create --name myenvname bioconductor-transformgampoi

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

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