Statistical Inference of Large-Scale Gaussian Graphical Model in Gene Networks R package SILGGM version 1.0.0 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. Software Depends: R (>= 3.0.0), Rcpp Imports: glasso, MASS, reshape, utils LinkingTo: Rcpp Rong Zhang <roz16@pitt.edu> Comprehensive R Archive Network (CRAN) Rong Zhang, Zhao Ren and Wei Chen GPL (>= 2) 2017-10-16 application/tgz https://CRAN.R-project.org/package=SILGGM doi:10.32614/CRAN.package.SILGGM