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) ) (2) the de-sparsified nodewise scaled Lasso (Jankova and van de Geer (2017) ) (3) the de-sparsified graphical Lasso (Jankova and van de Geer (2015) ) (4) the GGM estimation with false discovery rate control (FDR) using scaled Lasso or Lasso (Liu (2013) ). Windows users should install 'Rtools' before the installation of this package.


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install.packages("SILGGM")

1.0.0 by Rong Zhang, a year ago


Browse source code at https://github.com/cran/SILGGM


Authors: Rong Zhang , Zhao Ren and Wei Chen


Documentation:   PDF Manual  


GPL (>= 2) license


Imports glasso, MASS, reshape, utils

Depends on Rcpp

Linking to Rcpp


See at CRAN