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sparsenetgls

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


Bioconductor version: Release (3.18)

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, enter citation("sparsenetgls")):

Installation

To install this package, start R (version "4.3") and enter:


if (!require("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install("sparsenetgls")

For older versions of R, please refer to the appropriate Bioconductor release.

Documentation

To view documentation for the version of this package installed in your system, start R and enter:

browseVignettes("sparsenetgls")
Introduction to sparsenetgls HTML R Script
Reference Manual PDF
NEWS Text

Details

biocViews CopyNumberVariation, GraphAndNetwork, ImmunoOncology, MassSpectrometry, Metabolomics, Proteomics, Regression, Software, Visualization
Version 1.20.0
In Bioconductor since BioC 3.8 (R-3.5) (5.5 years)
License GPL-3
Depends R (>= 4.0.0), Matrix, MASS
Imports methods, glmnet, huge, stats, graphics, utils
System Requirements GNU make
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Suggests testthat, lme4, BiocStyle, knitr, rmarkdown, roxygen2 (>= 5.0.0)
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Package Archives

Follow Installation instructions to use this package in your R session.

Source Package sparsenetgls_1.20.0.tar.gz
Windows Binary sparsenetgls_1.20.0.zip (64-bit only)
macOS Binary (x86_64) sparsenetgls_1.20.0.tgz
macOS Binary (arm64) sparsenetgls_1.20.0.tgz
Source Repository git clone https://git.bioconductor.org/packages/sparsenetgls
Source Repository (Developer Access) git clone git@git.bioconductor.org:packages/sparsenetgls
Bioc Package Browser https://code.bioconductor.org/browse/sparsenetgls/
Package Short Url https://bioconductor.org/packages/sparsenetgls/
Package Downloads Report Download Stats