Tuo Zhao <tourzhao@andrew.cmu.edu>, Han Liu
<hanliu@cs.jhu.edu>
The package "huge" provides a general framework for
high-dimensional undirected graph estimation. The
packageintegrates data preprocessing (Gaussianization), graph
screening, graph estimation, and model selection techniques
into a pipeline. The NonparaNormal(NPN) transformation is
applied to preprocess the data and helps relax the normality
assumption. The Graph SURE Screening (GSS) subroutine
preselects the graph neighborhood of each variable. In the
graph estimation stage, the structure of either the whole graph
or a pre-specified sub-graph is estimated by the Meinshausen &
Buhlmann Graph Estimation via Lasso (GEL) strategy on the
pre-screened data. In the case d >> n, the computation is
memory optimized and is targeted on larger-scale problems (with
d>3000). We also provide another efficient method, Graph
Approximation via Correlation Thresholding(GACT). Three
regularization parameter selection methods are included in this
package: (1) StARS: Stability Approach for Regularization
Selection (2) PIC: Permutation Information Criterion (3)
Extended Bayesian Information Criterion (EBIC) based on
pseudo-likelihood.

The package "huge" provides a general framework for
high-dimensional undirected graph estimation. The package
integrates data preprocessing (Gaussianization), graph
screening, graph estimation, and model selection techniques
into a pipeline. The NonparaNormal(NPN) transformation is
applied to preprocess the data and helps relax the normality
assumption. The Graph SURE Screening (GSS) subroutine
preselects the graph neighborhood of each variable. In the
graph estimation stage, the structure of either the whole graph
or a pre-specified sub-graph is estimated by the Meinshausen &
Buhlmann Graph Estimation via Lasso (GEL) strategy on the
pre-screened data. In the case d >> n, the computation is
memory optimized and is targeted on larger-scale problems (with
d>3000). We also provide another efficient method, Graph
Approximation via Correlation Thresholding. Three
regularization parameter selection methods are included in this
package: (1) StARS: Stability Approach for Regularization
Selection (2) PIC: Permutation Information Criterion (3)
Extended Bayesian Information Criterion (EBIC) based on
pseudo-likelihood.