recipe r-easypar

The easypar package makes it easy to implement parallel computations in R. To use this package, you need to have a function that carries out your desired computation. easypar will take care of the burden of turning that function into a runnable parallel piece of code, offering a soilution based on the foreach and doParallel paradigms for parallel computing, or generating array jobs for the popular LSF workload for distributed high performance computing.

Homepage:

https://caravagnalab.github.io/easypar/

Developer docs:

https://github.com/caravagn/easypar

License:

GPL3 / GPL-3.0-or-later

Recipe:

/r-easypar/meta.yaml

package r-easypar

(downloads) docker_r-easypar

versions:

1.0.0-0

depends r-base:

>=4.4,<4.5.0a0

depends r-cli:

depends r-crayon:

depends r-doparallel:

depends r-dplyr:

depends r-foreach:

depends r-markdown:

depends r-prettydoc:

depends r-progress:

depends r-readr:

depends r-tibble:

requirements:

additional platforms:

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 r-easypar

and update with::

   mamba update r-easypar

To create a new environment, run:

mamba create --name myenvname r-easypar

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/r-easypar:<tag>

(see `r-easypar/tags`_ for valid values for ``<tag>``)

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Link to this page

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