The huge package for high-dimensional undirected graph estimation in R

Tuo Zhao, Han Liu, Kathryn Roeder, John Lafferty, Larry Wasserman

Research output: Contribution to journalArticle

157 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)1059-1062
Number of pages4
JournalJournal of Machine Learning Research
Volume13
StatePublished - Apr 2012

All Science Journal Classification (ASJC) codes

  • Software
  • Control and Systems Engineering
  • Statistics and Probability
  • Artificial Intelligence

Keywords

  • Data-dependent model selection
  • Glasso
  • High-dimensional undirected graph estimation
  • Huge
  • Lossless screening
  • Lossy screening
  • Semiparametric graph estimation

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  • Cite this

    Zhao, T., Liu, H., Roeder, K., Lafferty, J., & Wasserman, L. (2012). The huge package for high-dimensional undirected graph estimation in R. Journal of Machine Learning Research, 13, 1059-1062.