print.huge

An object with S3 class <code>"huge"</code>

Print the information about the model usage, the solution path length, subgraph dimension, fullgraph dimension, sparsity level

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.

print.huge: Print function for S3 class "huge"

Description

Usage

Arguments

See Also