sparsenetgls

DOI: 10.18129/B9.bioc.sparsenetgls    

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 regression

Bioconductor version: Development (3.9)

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 <i.zeng at auckland.ac.nz>

Citation (from within R, enter citation("sparsenetgls")):

Installation

To install this package, start R (version "3.6") and enter:

if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")
BiocManager::install("sparsenetgls", version = "3.9")

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, MassSpectrometry, Metabolomics, Proteomics, Regression, Software, Visualization
Version 1.1.0
License GPL-3
Depends R (>= 3.5.0), Matrix, MASS
Imports methods, glmnet, parcor, 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
Suggests Me
Links To Me
Build Report  

Package Archives

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

Source Package sparsenetgls_1.1.0.tar.gz
Windows Binary sparsenetgls_1.1.0.zip
Mac OS X 10.11 (El Capitan) sparsenetgls_1.1.0.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 http://bioconductor.org/packages/sparsenetgls/
Package Downloads Report Download Stats

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