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

By Tuo Zhao, Han Liu, Kathryn Roeder, John D. Lafferty, Larry A. Wasserman. Journal of machine learning research, 2012

531 citations recorded by the source corpus.

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.

View Paper: https://www.semanticscholar.org/paper/d65edfe2fb8127587cc0051cd20f220e3a2f2554

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Citations in the corpus

Structure Learning in Graphical Modeling (2016, methodology): “Ignoring heterogeneity across the n = 804 samples, we apply neighborhood selection in a Gaussian copula model (see Sections 3.4 and 3.6), under the defaults of the software of Zhao et al. (2012).”
High-dimensional consistency in score-based and hybrid structure learning (2015, methodology): “We estimate the CIG for ARGES-CIG using neighborhood selection with the LASSO(2) of Meinshausen and Bühlmann [2006], where we use the implementation in the R-package huge [Zhao et al., 2012].”
Sparse and Compositionally Robust Inference of Microbial Ecological Networks (2014, methodology): “The SPIEC-EASI inference engine relies on the R package huge [49], which includes algorithms to solve neighborhood and covariance selection problems [20, 22], as well as the StARS model selection.”
stm: An R Package for Structural Topic Models (2019, unknown): “2008), stringr (Wickham 2017), SnowballC (Bouchet-Valat 2014), , igraph (Csardi and Nepusz 2006), huge (Zhao et al. 2012), clue (Hornik 2005), wordcloud (Fellows 2014), KernSmooth (Wand 2015), geometry (Habel et al.”
Efficient Dimensionality Reduction for High-Dimensional Network Estimation (2014, methodology): “We implemented MGL in C, and we used the C source code of CRAN R package huge (Zhao et al., 2012) to estimate the inverse covariance matrix of the latent variables ( Sec.”
Learning structured densities via infinite dimensional exponential families (2015, methodology): “We use the same sampling method as in [31] to generate the data: we set Ωs = 0.”
Computer-Assisted Text Analysis for Comparative Politics (2015, methodology): “The authors note that this selection approach has strong empirical performance but is sensitive to under-selection of edges (Zhao et al., 2012).”
mgm: Estimating Time-Varying Mixed Graphical Models in High-Dimensional Data (2015, methodology): “…Graphical Models (GGMs): the R - packages glasso (Friedman and Tibshirani 2014) and huge (Zhao, Li, Liu, Roeder, Laﬀerty, and Wasserman 2015; Zhao, Liu, Roeder, Laﬀerty, and Wasserman 2012) implement the graphical lasso (Banerjee, El Ghaoui, and d’Aspremont 2008; Friedman, Hastie, and…”