Package: huge
Type: Package
Title: High-dimensional Undirected Graph Estimation
Version: 1.0
Date: 2011-02-28
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 (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
only for GLASSO).
License: GPL-2
Repository: CRAN
Date/Publication: 2011-03-02 17:32:21
Packaged: 2011-03-02 16:20:13 UTC; tourzhao

Package: huge
Type: Package
Title: High-dimensional Undirected Graph Estimation
Version: 1.1.0
Date: 2011-07-19
Author: Tuo Zhao, Han Liu, Kathryn Roeder, John Lafferty, Larry
Wasserman
Maintainer: Tuo Zhao <tzhao5@jhu.edu>, Han Liu <hanliu@cs.jhu.edu>
Depends: Matrix, lattice, igraph, MASS
Description: The package "huge" provides a general framework for
high-dimensional undirected graph estimation. It integrates
data preprocessing, 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
Meinshausen-Buhlmann graph estimation or the graphical lasso,
and both methods can be further accelerated by the lossy
screening rule preselecting the neighborhood of each variable
by correlation thresholding. We target on high-dimensional data
analysis usually d >> n, and the computation is
memory-optimized using the sparse matrix output. We also
provide a computationally efficient approach, correlation
thresholding graph estimation. Three
regularization/thresholding parameter selection methods are
included in this package: (1)stability approach for
regularization selection (2) rotation information criterion (3)
extended Bayesian information criterion which is only available
for the graphical lasso.
License: GPL-2
Repository: CRAN
Date/Publication: 2011-07-23 13:55:10
Packaged: 2011-07-20 03:50:41 UTC; tourzhao