High-dimensional Undirected Graph Estimation
Description
The package "huge" provides a general framework for
high-dimensional undirected graph estimation. It integrates
data preprocessing (Gaussianization), 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 the Meinshausen & Buhlmann Graph Estimation via
Lasso (MBGEL) by default and it can be further accelerated by
the Graph SURE Screening (GSS) subroutine which preselects the
graph neighborhood of each variable. In the case d >> n, the
computation is memory optimized and is targeted on larger-scale
problems. We also provide two alternative approaches for the
graph estimation stage:(1) Graph Estimation via Correlation
Thresholding (GECT) which is highly efficient and (2) A
slightly modified Graphical Lasso (GLASSO) procedure in which
the memory usage is optimized using sparse matrix output. Three
regularization/thresholding parameter selection methods are
included in this package: (1) StARS: Stability Approach for
Regularization Selection (2) RIC: Rotation Information
Criterion (3) Extended Bayesian Information Criterion (EBIC
only for GLASSO).