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
