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 49 articles , 1 standard article )

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  1. Osborne, Nathan; Peterson, Christine B.; Vannucci, Marina: Latent network estimation and variable selection for compositional data via variational EM (2022)
  2. Peeters, C. F. W., Bilgrau, A. E., van Wieringen, W. N. : rags2ridges: A One-Stop-l2-Shop for Graphical Modeling of High-Dimensional Precision Matrices (2022) not zbMATH
  3. Byrd, Michael; Nghiem, Linh H.; McGee, Monnie: Bayesian regularization of Gaussian graphical models with measurement error (2021)
  4. Fernando Palluzzi, Mario Grassi: SEMgraph: An R Package for Causal Network Analysis of High-Throughput Data with Structural Equation Models (2021) arXiv
  5. Park, Seongoh; Wang, Xinlei; Lim, Johan: Estimating high-dimensional covariance and precision matrices under general missing dependence (2021)
  6. Rossell, David; Zwiernik, Piotr: Dependence in elliptical partial correlation graphs (2021)
  7. Stephenson, Matthew; Ali, R. Ayesha; Darlington, Gerarda A.; Schenkel, Flavio S.; Squires, E. James: DSLRIG: leveraging predictor structure in logistic regression (2021)
  8. Zhang, Rong; Ren, Zhao; Celedón, Juan C.; Chen, Wei: Inference of large modified Poisson-type graphical models: application to RNA-seq data in childhood atopic asthma studies (2021)
  9. Augugliaro, Luigi; Sottile, Gianluca; Vinciotti, Veronica: The conditional censored graphical Lasso estimator (2020)
  10. Boudt, Kris; Rousseeuw, Peter J.; Vanduffel, Steven; Verdonck, Tim: The minimum regularized covariance determinant estimator (2020)
  11. Jonas M. B. Haslbeck, Lourens J. Waldorp: mgm: Estimating Time-Varying Mixed Graphical Models in High-Dimensional Data (2020) not zbMATH
  12. Li, Qiong; Gao, Xin; Massam, Hélène: Bayesian model selection approach for coloured graphical Gaussian models (2020)
  13. Li, Tianxi; Qian, Cheng; Levina, Elizaveta; Zhu, Ji: High-dimensional Gaussian graphical models on network-linked data (2020)
  14. Son, Sungtaek; Park, Cheolwoo; Jeon, Yongho: Sparse graphical models via calibrated concave convex procedure with application to fMRI data (2020)
  15. Abbruzzo, Antonino; Vujačić, Ivan; Mineo, Angelo M.; Wit, Ernst C.: Selecting the tuning parameter in penalized Gaussian graphical models (2019)
  16. Julien Chiquet, Pierre Barbillon, Timothée Tabouy: missSBM: An R Package for Handling Missing Values in the Stochastic Block Model (2019) arXiv
  17. Li, Zehang Richard; McCormick, Tyler H.: An expectation conditional maximization approach for Gaussian graphical models (2019)
  18. Margaret Roberts; Brandon Stewart; Dustin Tingley: stm: An R Package for Structural Topic Models (2019) not zbMATH
  19. Müller, Dominik; Czado, Claudia: Dependence modelling in ultra high dimensions with vine copulas and the graphical lasso (2019)
  20. Sanguinetti, Guido (ed.); Huynh-Thu, Vân Anh (ed.): Gene regulatory networks. Methods and protocols (2019)

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