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

References in zbMATH (referenced in 17 articles , 1 standard article )

Showing results 1 to 17 of 17.
Sorted by year (citations)

  1. Liu, Han; Wang, Lie: TIGER: A tuning-insensitive approach for optimally estimating Gaussian graphical models (2017)
  2. Bar-Hen, Avner; Poggi, Jean-Michel: Influence measures and stability for graphical models (2016)
  3. Kruppa, Jochen; Kramer, Frank; Beißbarth, Tim; Jung, Klaus: A simulation framework for correlated count data of features subsets in high-throughput sequencing or proteomics experiments (2016)
  4. Lin, Jiahe; Basu, Sumanta; Banerjee, Moulinath; Michailidis, George: Penalized maximum likelihood estimation of multi-layered Gaussian graphical models (2016)
  5. Banerjee, Sayantan; Ghosal, Subhashis: Bayesian structure learning in graphical models (2015)
  6. Croux, Christophe; Öllerer, Viktoria: Comments on: “Robust estimation of multivariate location and scatter in the presence of cellwise and casewise contamination” (2015)
  7. Li, Xingguo; Zhao, Tuo; Yuan, Xiaoming; Liu, Han: The flare package for high dimensional linear regression and precision matrix estimation in R (2015)
  8. Mohammadi, A.; Wit, E.C.: BDgraph: An R Package for Bayesian Structure Learning in Graphical Models (2015) arXiv
  9. Mohammadi, A.; Wit, E.C.: Bayesian structure learning in sparse Gaussian graphical models (2015)
  10. Paganoni, Anna Maria (ed.); Secchi, Piercesare (ed.): Advances in complex data modeling and computational methods in statistics. Selected papers based on the presentations at the conference “S.Co 2013, Complex data modeling and computationally intensive methods for estimation and prediction”, Milano, Italy, September 9--12, 2013 (2015)
  11. Pircalabelu, Eugen; Claeskens, Gerda; Jahfari, Sara; Waldorp, Lourens J.: A focused information criterion for graphical models in fMRI connectivity with high-dimensional data (2015)
  12. Pircalabelu, Eugen; Claeskens, Gerda; Waldorp, Lourens: A focused information criterion for graphical models (2015)
  13. Pang, Haotian; Liu, Han; Vanderbei, Robert: The FASTCLIME package for linear programming and large-scale precision matrix estimation in R (2014)
  14. Han, Fang; Zhao, Tuo; Liu, Han: CODA: high dimensional copula discriminant analysis (2013)
  15. Pourahmadi, Mohsen: High-dimensional covariance estimation (2013)
  16. Liu, Han; Han, Fang; Yuan, Ming; Lafferty, John; Wasserman, Larry: High-dimensional semiparametric Gaussian copula graphical models (2012)
  17. Zhao, Tuo; Liu, Han; Roeder, Kathryn; Lafferty, John; Wasserman, Larry: The huge package for high-dimensional undirected graph estimation in R (2012)