The huge Package for High-dimensional Undirected Graph Estimation in R
This is an incremental software package for researchers and practitioners in statistics and data science needing tools for high-dimensional graph estimation.
The authors tackled the problem of estimating high-dimensional undirected graphs from data by developing an R package called huge, which implements existing methods from the literature and provides extra features such as support for semiparametric Gaussian copula models, improved scalability with screening rules, and corrections to convergence issues.
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.