This is the development version of sparsenetgls; for the stable release version, see sparsenetgls.
Using Gaussian graphical structue learning estimation in generalized least squared regression for multivariate normal regressionBioconductor version: Development (3.22)
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.
Author: Irene Zeng [aut, cre], Thomas Lumley [ctb]
Maintainer: Irene Zeng <szen003 at aucklanduni.ac.nz>
Citation (from within R, entercitation("sparsenetgls")): Installation
To install this package, start R (version "4.5") and enter:
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
# The following initializes usage of Bioc devel
BiocManager::install(version='devel')
BiocManager::install("sparsenetgls")
For older versions of R, please refer to the appropriate Bioconductor release.
DocumentationTo view documentation for the version of this package installed in your system, start R and enter:
browseVignettes("sparsenetgls")Follow Installation instructions to use this package in your R session.
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