Package: sparsenetgls Type: Package Title: Using Gaussian graphical structue learning estimation in generalized least squared regression for multivariate normal regression Version: 1.31.0 Authors@R: c(person("Irene", "Zeng", role = c("aut", "cre"), email = "szen003@aucklanduni.ac.nz"), person("Thomas", "Lumley", role = "ctb", email = "t.lumey@auckland.ac.nz")) Description: 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. License: GPL-3 Encoding: UTF-8 LazyData: true Depends: R (>= 4.0.0), Matrix, MASS Imports: methods, glmnet, huge, stats, graphics, utils Suggests: testthat, lme4, BiocStyle, knitr, rmarkdown, roxygen2 (>= 5.0.0) NeedsCompilation: no URL: RoxygenNote: 6.0.1 biocViews: ImmunoOncology, GraphAndNetwork,Regression,Metabolomics,CopyNumberVariation,MassSpectrometry,Proteomics,Software,Visualization bugReport: https://github.com/superOmics/sparsenetgls/issues VignetteBuilder: knitr SystemRequirements: GNU make Config/pak/sysreqs: libglpk-dev make libxml2-dev Repository: https://bioc.r-universe.dev Date/Publication: 2026-04-28 12:48:41 UTC RemoteUrl: https://github.com/bioc/sparsenetgls RemoteRef: HEAD RemoteSha: 4baecb048db05ce38811b569adf497e3be59a81c Packaged: 2026-05-30 09:49:24 UTC; root Author: Irene Zeng [aut, cre], Thomas Lumley [ctb] Maintainer: Irene Zeng