- 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:
- Developer docs:
- License:
GPL3 / GPL-3.0-or-later
- Recipe:
- package r-easypar¶
- Versions:
1.0.0-0- Depends:
on r-base
>=4.4,<4.5.0a0on r-cli
on r-crayon
on r-doparallel
on r-dplyr
on r-foreach
on r-markdown
on r-prettydoc
on r-progress
on r-readr
on r-tibble
- Additional platforms:
Installation¶
You need a conda-compatible package manager (currently either pixi, conda, or micromamba) and the Bioconda channel already activated (see Usage). Below, we show how to install with either pixi or conda (for micromamba and mamba, commands are essentially the same as with conda).
Pixi¶
With pixi installed and the Bioconda channel set up (see Usage), to install globally, run:
pixi global install r-easypar
to add into an existing workspace instead, run:
pixi add r-easypar
In the latter case, make sure to first add bioconda and conda-forge to the channels considered by the workspace:
pixi workspace channel add conda-forge
pixi workspace channel add bioconda
Conda¶
With conda installed and the Bioconda channel set up (see Usage), to install into an existing and activated environment, run:
conda install r-easypar
Alternatively, to install into a new environment, run:
conda create -n envname r-easypar
with envname being the name of the desired environment.
Container¶
Alternatively, every Bioconda package is available as a container image for usage with your preferred container runtime. For e.g. docker, run:
docker pull quay.io/biocontainers/r-easypar:<tag>
(see r-easypar/tags for valid values for <tag>).
Integrated deployment¶
Finally, note that many scientific workflow management systems directly integrate both conda and container based software deployment. Thus, workflow steps can be often directly annotated to use the package, leading to automatic deployment by the respective workflow management system, thereby improving reproducibility and transparency. Check the documentation of your workflow management system to find out about the integration.
Download stats¶
Link to this page¶
Render an 
badge with the following MarkDown:
[](http://bioconda.github.io/recipes/r-easypar/README.html)
