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J Mach Learn Res. 2012 Apr;13:1059-1062.

The huge Package for High-dimensional Undirected Graph Estimation in R.

Author information

1
Department of Computer Science, Johns Hopkins University, Baltimore, MD 21218, USA, TOURZHAO@JHU.EDU.
2
Department of Statistics, Carnegie Mellon University, Pittsburgh, PA, 15213, ROEDER@STAT.CMU.EDU.

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.

KEYWORDS:

data-dependent model selection; glasso; high-dimensional undirected graph estimation; huge; lossless screening; lossy screening; semiparametric graph estimation

PMID:
26834510
PMCID:
PMC4729207

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