lightbeam: Fast inference of a conditional independence graph

In beam: Fast Bayesian Inference in Large Gaussian Graphical Models

Description

Fast and memory efficient reconstruction of large conditional independence networks.

Usage

lightbeam(X, thres = 0.1, method = "holm", verbose=TRUE)

Arguments

X	n by p data matrix
thres	numeric. Significance threshold to be applied on adjusted tail probabilities.
method	character. Method to use for multiple comparison adjustment of tail probabilities.
verbose	logical. Whether information on progress should be be printed.

Details

The function returns a sparse upper triangular matrix containing the (adjusted) tails probabilities that are below the given threshold (i.e. significant).

Value

An object of class dgCMatrix-class

Author(s)

Gwenael G.R. Leday

References

Leday, G.G.R. and Richardson, S. (2019). Fast Bayesian inference in large Gaussian graphical models. Biometrics.

Examples

# Load data
data(TCPAprad)

# beam
res <- lightbeam(X = TCPAprad, thres=0.1)

beam documentation built on July 1, 2020, 10:23 p.m.