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 structure of
either the whole graph or a pre-specified sub-graph is
estimated by the Meinshausen & Buhlmann Graph Estimation via
Lasso (GEL) 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 (with d>2000). We also provide two alternative
approaches for the graph estimation stage:(1) Graph
Approximation via Correlation Thresholding (GACT) 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) PIC:
Permutation Information Criterion (3) Extended Bayesian
Information Criterion (EBIC).