Package: SILGGM
Type: Package
Title: Statistical Inference of Large-Scale Gaussian Graphical Model in
        Gene Networks
Version: 1.0.0
Date: 2017-10-15
Author: Rong Zhang, Zhao Ren and Wei Chen
Maintainer: Rong Zhang <roz16@pitt.edu>
Description: Provides a general framework to perform statistical
        inference of each gene pair and global inference of whole-scale
        gene pairs in gene networks using the well known Gaussian
        graphical model (GGM) in a time-efficient manner. We focus on
        the high-dimensional settings where p (the number of genes) is
        allowed to be far larger than n (the number of subjects). Four
        main approaches are supported in this package: (1) the
        bivariate nodewise scaled Lasso (Ren et al (2015)
        <doi:10.1214/14-AOS1286>) (2) the de-sparsified nodewise scaled
        Lasso (Jankova and van de Geer (2017)
        <doi:10.1007/s11749-016-0503-5>) (3) the de-sparsified
        graphical Lasso (Jankova and van de Geer (2015)
        <doi:10.1214/15-EJS1031>) (4) the GGM estimation with false
        discovery rate control (FDR) using scaled Lasso or Lasso (Liu
        (2013) <doi:10.1214/13-AOS1169>). Windows users should install
        'Rtools' before the installation of this package.
License: GPL (>= 2)
Imports: glasso, MASS, reshape, utils
Depends: R (>= 3.0.0), Rcpp
LinkingTo: Rcpp
NeedsCompilation: yes
Packaged: 2026-10-14 10:57:26 UTC; root
Repository: https://cran.r-universe.dev
Date/Publication: 2017-10-16 10:49:17 UTC
RemoteUrl: https://github.com/cran/SILGGM
RemoteRef: HEAD
RemoteSha: 1b00328765549cc285ce38d32125ddd2bda8d050