Package: huge
Type: Package
Title: High-dimensional Undirected Graph Estimation
Version: 0.8
Date: 2010-11-12
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: MASS,Matrix, lattice, glmnet, igraph
Description: The package "huge" provides a general framework for
high-dimensional undirected graph estimation. The package
integrates data preprocessing (Gaussianization), graph
screening, graph estimation, and model selection techniques
into a pipeline. The NonparaNormal(NPN) transformation is
applied to preprocess the data and helps relax the normality
assumption. The Graph SURE Screening (GSS) subroutine
preselects the graph neighborhood of each variable. 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) strategy on the
pre-screened data. In the case d >> n, the computation is
memory optimized and is targeted on larger-scale problems (with
d>3000). We also provide another efficient method, Graph
Approximation via Correlation Thresholding. Three
regularization 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) based on
pseudo-likelihood.
License: GPL-2
Packaged: 2010-11-13 18:18:39 UTC; asnoppy
Repository: CRAN
Date/Publication: 2010-11-14 08:41:55

Package: huge
Type: Package
Title: High-dimensional Undirected Graph Estimation
Version: 0.9.1
Date: 2011-02-12
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
Date/Publication: 2011-02-13 16:11:47
Packaged: 2011-02-13 00:14:57 UTC; tourzhao