title: The huge Package for High-dimensional Undirected Graph Estimation in R.
abstract: We describe an R package named huge which provides easy-to-use functions for estimating high dimensional undirected graphs from data. This package implements recent results in the literature, including Friedman et al. (2007), Liu et al. (2009, 2012) and Liu et al. (2010). Compared with the existing graph estimation package glasso, the huge package provides extra features: (1) instead of using Fortan, it is written in C, which makes the code more portable and easier to modify; (2) besides fitting Gaussian graphical models, it also provides functions for fitting high dimensional semiparametric Gaussian copula models; (3) more functions like data-dependent model selection, data generation and graph visualization; (4) a minor convergence problem of the graphical lasso algorithm is corrected; (5) the package allows the user to apply both lossless and lossy screening rules to scale up large-scale problems, making a tradeoff between computational and statistical efficiency.
authors.affiliation: Department of Computer Science, Johns Hopkins University, Baltimore, MD 21218, USA, TOURZHAO@JHU.EDU.
authors.forename: Tuo
authors.initials: T
authors.lastname: Zhao
authors.forename: Han
authors.initials: H
authors.lastname: Liu
authors.affiliation: Department of Statistics, Carnegie Mellon University, Pittsburgh, PA, 15213, ROEDER@STAT.CMU.EDU.
authors.forename: Kathryn
authors.initials: K
authors.lastname: Roeder
authors.forename: John
authors.initials: J
authors.lastname: Lafferty
authors.forename: Larry
authors.initials: L
authors.lastname: Wasserman
keywords: data-dependent model selection; glasso; high-dimensional undirected graph estimation; huge; lossless screening; lossy screening; semiparametric graph estimation
references.citation: J Stat Softw. 2010;33(1):1-22
references.pmid: 20808728
references.citation: Biostatistics. 2008 Jul;9(3):432-41
references.pmid: 18079126
references.citation: Electron J Stat. 2012 Nov 9;6:2125-2149
references.pmid: 25558297
references.citation: J Mach Learn Res. 2012 Apr;13:1059-1062
references.pmid: 26834510
references.citation: Adv Neural Inf Process Syst. 2010 Dec 31;24(2):1432-1440
references.pmid: 25152607