huge: High-Dimensional Undirected Graph Estimation
Provides a general framework for high-dimensional undirected graph estimation. It integrates data preprocessing, neighborhood screening, graph estimation, and model selection techniques into a pipeline. In preprocessing stage, the nonparanormal(npn) transformation is applied to help relax the normality assumption. In the graph estimation stage, the graph structure is estimated by Meinshausen-Buhlmann graph estimation or the graphical lasso, and both methods can be further accelerated by the lossy screening rule preselecting the neighborhood of each variable by correlation thresholding. We target on high-dimensional data analysis usually d >> n, and the computation is memory-optimized using the sparse matrix output. We also provide a computationally efficient approach, correlation thresholding graph estimation. Three regularization/thresholding parameter selection methods are included in this package: (1)stability approach for regularization selection (2) rotation information criterion (3) extended Bayesian information criterion which is only available for the graphical lasso.
- Tuo Zhao, Xingguo Li, Han Liu, Kathryn Roeder, John Lafferty, Larry Wasserman
- Date of publication
- 2015-09-16 10:05:23
- Tuo Zhao <email@example.com>
- High-dimensional undirected graph estimation
- Data generator
- Internal huge functions
- Nonparanormal(npn) transformation
- High-Dimensional Undirected Graph Estimation
- Graph visualization
- Draw ROC Curve for a graph path
- Model selection for high-dimensional undirected graph...
- Plot function for S3 class "huge"
- Plot function for S3 class "roc"
- Plot function for S3 class "select"
- Plot function for S3 class "sim"
- Print function for S3 class "huge"
- Print function for S3 class "roc"
- Print function for S3 class "select"
- Print function for S3 class "sim"
- Stock price of S&P 500 companies from 2003 to 2008
Files in this package