recipe bioconductor-s4vectors

Foundation of vector-like and list-like containers in Bioconductor

Homepage:

https://bioconductor.org/packages/3.22/bioc/html/S4Vectors.html

License:

Artistic-2.0

Recipe:

/bioconductor-s4vectors/meta.yaml

Links:

biotools: s4vectors, doi: 10.1038/nmeth.3252

The S4Vectors package defines the Vector and List virtual classes and a set of generic functions that extend the semantic of ordinary vectors and lists in R. Package developers can easily implement vector-like or list-like objects as concrete subclasses of Vector or List. In addition, a few low-level concrete subclasses of general interest (e.g. DataFrame, Rle, Factor, and Hits) are implemented in the S4Vectors package itself (many more are implemented in the IRanges package and in other Bioconductor infrastructure packages).

package bioconductor-s4vectors

(downloads) docker_bioconductor-s4vectors

Versions:
0.48.0-1,  0.48.0-0,  0.44.0-2,  0.44.0-1,  0.44.0-0,  0.40.2-2,  0.40.2-1,  0.40.2-0,  0.38.1-0, 

0.48.0-1,  0.48.0-0,  0.44.0-2,  0.44.0-1,  0.44.0-0,  0.40.2-2,  0.40.2-1,  0.40.2-0,  0.38.1-0,  0.36.0-1,  0.36.0-0,  0.32.4-0,  0.32.3-0,  0.32.0-0,  0.30.0-0,  0.28.1-0,  0.28.0-0,  0.26.0-0,  0.24.0-0,  0.22.0-1,  0.20.1-0,  0.18.3-0,  0.16.0-0,  0.14.7-0,  0.12.2-0,  0.12.0-0,  0.10.3-0,  0.9.0-0,  0.8.11-1,  0.8.11-0,  0.8.7-0,  0.8.5-0,  0.8.1-0,  0.8.0-0,  0.6.6-0

Depends:

  • on bioconductor-biocgenerics >=0.56.0,<0.57.0

  • on bioconductor-biocgenerics >=0.56.0,<0.57.0a0

  • on libblas >=3.9.0,<4.0a0

  • on libgcc >=14

  • on liblapack >=3.9.0,<4.0a0

  • on liblzma >=5.8.2,<6.0a0

  • on libzlib >=1.3.1,<2.0a0

  • on r-base >=4.5,<4.6.0a0

Additional platforms:
linux-aarch64,  osx-arm64

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 bioconductor-s4vectors

to add into an existing workspace instead, run:

pixi add bioconductor-s4vectors

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 bioconductor-s4vectors

Alternatively, to install into a new environment, run:

conda create -n envname bioconductor-s4vectors

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/bioconductor-s4vectors:<tag>

(see bioconductor-s4vectors/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.

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