huge (1.0.3)

High-dimensional Undirected Graph Estimation.

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).

Maintainer: Tuo Zhao
Author(s): Tuo Zhao, Han Liu, Kathryn Roeder, John Lafferty, Larry Wasserman

License: GPL-2

Uses: glasso, igraph, lattice, MASS, Matrix
Reverse depends: BDgraph, glassomix, ldstatsHD, smart
Reverse suggests: CompareCausalNetworks, edgebundleR, LICORS, pcalg, pulsar, sand, smart, stabs, stm, themetagenomics

Released over 8 years ago.