sparsenetgls

DOI: 10.18129/B9.bioc.sparsenetgls    

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

Bioconductor version: Release (3.13)

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>

Installation

To install this package, start R (version "4.1") and enter:

if (!requireNamespace("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")

 

HTML	R Script	Introduction to sparsenetgls
PDF		Reference Manual
Text		NEWS

Details

biocViews	CopyNumberVariation, GraphAndNetwork, ImmunoOncology, MassSpectrometry, Metabolomics, Proteomics, Regression, Software, Visualization
Version	1.10.0
In Bioconductor since	BioC 3.8 (R-3.5) (3 years)
License	GPL-3
Depends	R (>= 4.0.0), Matrix, MASS
Imports	methods, glmnet, huge, stats, graphics, utils
LinkingTo
Suggests	testthat, lme4, BiocStyle, knitr, rmarkdown, roxygen2 (>= 5.0.0)
SystemRequirements	GNU make
Enhances
URL
Depends On Me
Imports Me
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Build Report

Package Archives

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

Source Package	sparsenetgls_1.10.0.tar.gz
Windows Binary	sparsenetgls_1.10.0.zip
macOS 10.13 (High Sierra)	sparsenetgls_1.10.0.tgz
Source Repository	git clone https://git.bioconductor.org/packages/sparsenetgls
Source Repository (Developer Access)	git clone git@git.bioconductor.org:packages/sparsenetgls
Package Short Url	https://bioconductor.org/packages/sparsenetgls/
Package Downloads Report	Download Stats