The huge Package for High-dimensional Undirected Graph Estimation in R
Carnegie Mellon University · Johns Hopkins University
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;…
Citation impact
- FWCI
- —
- Percentile
- —
- References
- 23
Authors
5- TZTuo ZhaoCorresponding
Carnegie Mellon University, Johns Hopkins University
- HLHan Liu
Johns Hopkins University, Carnegie Mellon University
- KRKathryn Roeder
Carnegie Mellon University, Johns Hopkins University
- JLJohn Lafferty
Carnegie Mellon University, Johns Hopkins University
- LWLarry Wasserman
Johns Hopkins University, Carnegie Mellon University
Topics & keywords
- Computer science
- Lossless compression
- R package
- Gaussian
- Lossy compression
- Graphical model
- Theoretical computer science
- Graph