Package: huge
Type: Package
Title: High-dimensional Undirected Graph Estimation
Version: 0.7
Date: 2010-11-09
Author: Tuo Zhao, Han Liu, Kathryn Roeder, John Lafferty, Larry
Wasserman
Maintainer: Tuo Zhao <tourzhao@gmail.com>, 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 transformation is applied to
preprocess the data and helps relax the normality assumption.
The graph screening 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-sclae problems (with d>3000). We also
provide another efficient method, Graph Estimation via
Correlation Approximation (GECA). Two regularization parameter
selection methods are included in this package: (1) StARS:
stability approach for regularization selection (2) extended
Bayesian information criterion (BIC) based on
pseudo-likelihood.
License: GPL-2
Packaged: 2010-11-10 15:32:18 UTC; johndoe
Repository: CRAN
Date/Publication: 2010-11-11 12:40:37

Package: huge
Type: Package
Title: High-dimensional Undirected Graph Estimation
Version: 0.8.1
Date: 2010-11-15
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
packageintegrates 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(GACT). 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
Repository: CRAN
Packaged: 2010-11-17 05:51:00 UTC; asnoppy
Date/Publication: 2010-11-17 08:17:16