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 pitt.edu>
License:	GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation:	yes
CRAN checks:	SILGGM results

Documentation:

Reference manual:

SILGGM.pdf

Downloads:

Package source:	SILGGM_1.0.0.tar.gz
Windows binaries:	r-devel: SILGGM_1.0.0.zip, r-release: SILGGM_1.0.0.zip, r-oldrel: SILGGM_1.0.0.zip
macOS binaries:	r-release (arm64): SILGGM_1.0.0.tgz, r-oldrel (arm64): SILGGM_1.0.0.tgz, r-release (x86_64): SILGGM_1.0.0.tgz, r-oldrel (x86_64): SILGGM_1.0.0.tgz

Reverse dependencies:

Reverse imports:

noisysbmGGM

Linking:

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