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

TL;DR

The huge R package offers comprehensive tools for estimating high-dimensional undirected graphs, incorporating recent advances, improved efficiency, and additional features over existing packages like glasso.

Contribution

It introduces a portable C implementation, supports semiparametric Gaussian copula models, and enhances scalability and usability for high-dimensional graph estimation.

Findings

Provides a more portable and modifiable implementation in C.

Supports semiparametric Gaussian copula models.

Enables scalable graph estimation with screening rules.

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.