Zhao, Tuo; Liu, Han; Roeder, Kathryn; Lafferty, John; Wasserman, Larry The huge package for high-dimensional undirected graph estimation in R. (English) Zbl 1283.68311 J. Mach. Learn. Res. 13, 1059-1062 (2012). Summary: 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. Cited in 40 Documents MSC: 68T05 Learning and adaptive systems in artificial intelligence 62-04 Software, source code, etc. for problems pertaining to statistics 68-04 Software, source code, etc. for problems pertaining to computer science 68N15 Theory of programming languages Keywords:high-dimensional undirected graph estimation; glasso; huge; semiparametric graph estimation; data-dependent model selection; lossless screening; lossy screening Software:R; glasso; huge PDFBibTeX XMLCite \textit{T. Zhao} et al., J. Mach. Learn. Res. 13, 1059--1062 (2012; Zbl 1283.68311) Full Text: arXiv Link