recipe bioconductor-sparsenetgls

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






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



Required By


With an activated Bioconda channel (see 2. Set up channels), install with:

conda install bioconductor-sparsenetgls

and update with:

conda update bioconductor-sparsenetgls

or use the docker container:

docker pull<tag>

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