recipe fastlmm

Fast GWAS

Homepage:

http://research.microsoft.com/en-us/um/redmond/projects/mscompbio/fastlmm/

License:

Apache 2.0

Recipe:

/fastlmm/meta.yaml

package fastlmm

versions:

0.2.32-5,  0.2.32-4,  0.2.32-3,  0.2.32-2,  0.2.32-1,  0.2.32-0,  0.2.24-0

depends dill:

depends libgcc-ng:

>=10.3.0

depends libstdcxx-ng:

>=10.3.0

depends matplotlib:

>=1.4.3

depends numpy:

>=1.9.3

depends pandas:

>=0.16.2

depends pysnptools:

>=0.3.13

depends python:

>=2.7,<2.8.0a0

depends python_abi:

2.7.* *_cp27mu

depends scikit-learn:

>=0.16.1,<0.20

depends scipy:

>=0.16.0

depends statsmodels:

>=0.6.1

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 fastlmm

and update with::

   mamba update fastlmm

To create a new environment, run:

mamba create --name myenvname fastlmm

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/fastlmm:<tag>

(see `fastlmm/tags`_ for valid values for ``<tag>``)

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Link to this page

Render an badge with the following MarkDown:

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