- recipe bioconductor-sparsearray
Efficient in-memory representation of multidimensional sparse arrays
- Homepage:
https://bioconductor.org/packages/3.18/bioc/html/SparseArray.html
- License:
Artistic-2.0
- Recipe:
The SparseArray package is an infrastructure package that provides an array-like container for efficient in-memory representation of multidimensional sparse data in R. The package defines the SparseArray virtual class and two concrete subclasses: COO_SparseArray and SVT_SparseArray. Each subclass uses its own internal representation of the nonzero multidimensional data, the "COO layout" and the "SVT layout", respectively. SVT_SparseArray objects mimic as much as possible the behavior of ordinary matrix and array objects in base R. In particular, they suppport most of the "standard matrix and array API" defined in base R and in the matrixStats package from CRAN.
- package bioconductor-sparsearray¶
- versions:
1.2.2-1,1.2.2-0,1.0.10-0- depends bioconductor-biocgenerics:
>=0.48.0,<0.49.0- depends bioconductor-biocgenerics:
>=0.48.1,<0.49.0a0- depends bioconductor-iranges:
>=2.36.0,<2.37.0- depends bioconductor-iranges:
>=2.36.0,<2.37.0a0- depends bioconductor-matrixgenerics:
>=1.14.0,<1.15.0- depends bioconductor-matrixgenerics:
>=1.14.0,<1.15.0a0- depends bioconductor-s4arrays:
>=1.2.0,<1.3.0- depends bioconductor-s4arrays:
>=1.2.0,<2.0a0- depends bioconductor-s4vectors:
>=0.40.0,<0.41.0- depends bioconductor-s4vectors:
>=0.40.2,<0.41.0a0- depends bioconductor-xvector:
>=0.42.0,<0.43.0- depends bioconductor-xvector:
>=0.42.0,<1.0a0- depends libblas:
>=3.9.0,<4.0a0- depends libgcc-ng:
>=12- depends liblapack:
>=3.9.0,<4.0a0- depends r-base:
>=4.3,<4.4.0a0- depends r-matrix:
- depends r-matrixstats:
- requirements:
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 bioconductor-sparsearray and update with:: mamba update bioconductor-sparsearray
To create a new environment, run:
mamba create --name myenvname bioconductor-sparsearray
with
myenvnamebeing 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/bioconductor-sparsearray:<tag> (see `bioconductor-sparsearray/tags`_ for valid values for ``<tag>``)
Download stats¶
Link to this page¶
Render an 
badge with the following MarkDown:
[](http://bioconda.github.io/recipes/bioconductor-sparsearray/README.html)
