recipe bioconductor-sparsenetgls

Using Gaussian graphical structue learning estimation in generalized least squared regression for multivariate normal regression

Homepage:

https://bioconductor.org/packages/3.18/bioc/html/sparsenetgls.html

License:

GPL-3

Recipe:

/bioconductor-sparsenetgls/meta.yaml

The package provides methods of combining the graph structure learning and generalized least squares regression to improve the regression estimation. The main function sparsenetgls() provides solutions for multivariate regression with Gaussian distributed dependant variables and explanatory variables utlizing multiple well-known graph structure learning approaches to estimating the precision matrix, and uses a penalized variance covariance matrix with a distance tuning parameter of the graph structure in deriving the sandwich estimators in generalized least squares (gls) regression. This package also provides functions for assessing a Gaussian graphical model which uses the penalized approach. It uses Receiver Operative Characteristics curve as a visualization tool in the assessment.

package bioconductor-sparsenetgls

(downloads) docker_bioconductor-sparsenetgls

versions:
1.20.0-0,  1.18.0-0,  1.16.0-0,  1.12.0-0,  1.10.0-0,  1.8.0-1,  1.8.0-0,  1.6.0-0,  1.4.0-0, 

1.20.0-0,  1.18.0-0,  1.16.0-0,  1.12.0-0,  1.10.0-0,  1.8.0-1,  1.8.0-0,  1.6.0-0,  1.4.0-0,  1.2.0-1,  1.2.0-0,  1.0.1-0

depends r-base:

>=4.3,<4.4.0a0

depends r-glmnet:

depends r-huge:

depends r-mass:

depends r-matrix:

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-sparsenetgls

and update with::

   mamba update bioconductor-sparsenetgls

To create a new environment, run:

mamba create --name myenvname bioconductor-sparsenetgls

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/bioconductor-sparsenetgls:<tag>

(see `bioconductor-sparsenetgls/tags`_ for valid values for ``<tag>``)

Download stats

Link to this page

Render an install-with-bioconda badge with the following MarkDown:

[![install with bioconda](https://img.shields.io/badge/install%20with-bioconda-brightgreen.svg?style=flat)](http://bioconda.github.io/recipes/bioconductor-sparsenetgls/README.html)