Package: huge
Type: Package
Title: High-dimensional Undirected Graph Estimation
Version: 0.9
Date: 2010-11-21
Author: Tuo Zhao, Han Liu, Kathryn Roeder, John Lafferty, Larry
Wasserman
Maintainer: Tuo Zhao <tourzhao@andrew.cmu.edu>, Han Liu
<hanliu@cs.jhu.edu>
Depends: glmnet, glasso, igraph, MASS, Matrix, lattice,
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).
License: GPL-2
Repository: CRAN
Packaged: 2010-11-21 23:51:10 UTC; asnoppy
Date/Publication: 2010-11-22 07:50:42

Package: huge
Type: Package
Title: High-dimensional Undirected Graph Estimation
Version: 1.0.1
Date: 2011-04-10
Author: Tuo Zhao, Han Liu, Kathryn Roeder, John Lafferty, Larry
Wasserman
Maintainer: Tuo Zhao <tourzhao@andrew.cmu.edu>, Han Liu
<hanliu@cs.jhu.edu>
Depends: glmnet, glasso, igraph, MASS, Matrix, lattice,
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 (d>6000). 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).
License: GPL-2
Repository: CRAN
Date/Publication: 2011-04-11 06:35:59
Packaged: 2011-04-10 21:10:05 UTC; tourzhao