screen_shrink

The input data. Needs to be a num.samples by dim.samples matrix.

Include mean in likelihood. TRUE / FALSE (default).

include.mean

None / linear.growth (default) / sqrt.growth

trunc.method

truncation constant, number of samples per predictor (default=5)

trunc.k

Shrinkage approach for estimating Gaussian graphical model

package

internal

Export

examples

Removed

fixed.

n=50

parcor

until

wihat=screen_shrink(x)$wi

x=matrix(rnorm(n*p)

Package nethet is an implementation of statistical solid
    methodology enabling the analysis of network heterogeneity from
    high-dimensional data. It combines several implementations of recent
    statistical innovations useful for estimation and comparison of networks in
    a heterogeneous, high-dimensional setting. In particular, we provide code
    for formal two-sample testing in Gaussian graphical models (differential
    network and GGM-GSA; Stadler and Mukherjee, 2013, 2014) and make a novel
    network-based clustering algorithm available (mixed graphical lasso,
    Stadler and Mukherjee, 2013).

screen_shrink: Shrinkage approach for estimating Gaussian graphical model

Description

Usage

Arguments

Value