SILGGM: Statistical Inference of Large-Scale Gaussian Graphical Model in Gene Networks

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.

Version: 1.0.0
Depends: R (≥ 3.0.0), Rcpp
Imports: glasso, MASS, reshape, utils
LinkingTo: Rcpp
Published: 2017-10-16
Author: Rong Zhang, Zhao Ren and Wei Chen
Maintainer: Rong Zhang <roz16 at>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: yes
CRAN checks: SILGGM results


Reference manual: SILGGM.pdf
Package source: SILGGM_1.0.0.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
OS X El Capitan binaries: r-release: SILGGM_1.0.0.tgz
OS X Mavericks binaries: r-oldrel: SILGGM_1.0.0.tgz


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