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SILGGM
View on CRAN: Click
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Download and install SILGGM package within the R console
Install from CRAN:
install.packages("SILGGM")
Install from Github:
library("remotes")
install_github("cran/SILGGM")
Install by package version:
library("remotes")
install_version("SILGGM", "1.0.0")
Attach the package and use:
library("SILGGM")
Maintained by
Rong Zhang
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2017-10-16
Latest Update: 2017-10-16
Description:
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.
How to cite:
Rong Zhang (2017). SILGGM: Statistical Inference of Large-Scale Gaussian Graphical Model in Gene Networks. R package version 1.0.0, https://cran.r-project.org/web/packages/SILGGM. Accessed 27 Jan. 2025.
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Complete documentation for SILGGM
Functions, R codes and Examples using
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Some associated functions: SILGGM .
Some associated R codes: RcppExports.R . SILGGM.R . Full SILGGM package functions and examples
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