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:
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