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: 2017-10-15 16:58:07 UTC; Rong Zhang
Repository: CRAN
Date/Publication: 2017-10-16 11:49:17 UTC
Built: R 4.6.0; x86_64-apple-darwin20; 2026-04-27 03:18:17 UTC; unix
Archs: SILGGM.so.dSYM
