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

2020/06/26 by Tuo Zhao, Han Liu, Zhao, Tuo +7 · 3 citations
Computer Science · Mathematics · #Bayesian Modeling and Causal Inference #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Optimization and Control (math.OC) #Statistical Methods and Bayesian Inference #Statistical Methods and Inference #cs.LG #math.OC #stat.ML

paper · pdf · doi:10.48550/arxiv.2006.14781

Published on JMLR in 2012

arxiv created 2020/06/26 · openalex publication_date 2020/06/26 · arxiv updated 2020/06/29 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

Citations

• Stability Approach to Regularization Selection (StARS) for High Dimensional Graphical Models

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