depna: Dependency Neural Networks

In AlexChristensen/NetworkToolbox: Methods and Measures for Brain, Cognitive, and Psychometric Network Analysis

Dependency Neural Networks

Description

Applies the dependency network approach to neural network array

Usage

depna(neuralarray, cores, ...)

Arguments

neuralarray	Array from convertConnBrainMat function
cores	Numeric. Number of cores to use in computing results. Set to 1 to not use parallel computing. Recommended to use maximum number of cores minus one
...	Additional arguments from depend function

Value

Returns an array of n x n x m dependency matrices

Author(s)

Alexander Christensen <[e-mail]>

References

Jacob, Y., Winetraub, Y., Raz, G., Ben-Simon, E., Okon-Singer, H., Rosenberg-Katz, K., ... & Ben-Jacob, E. (2016). Dependency Network Analysis (DEPNA) reveals context related influence of brain network nodes. Scientific Reports, 6, 27444.

Kenett, D. Y., Tumminello, M., Madi, A., Gur-Gershgoren, G., Mantegna, R. N., & Ben-Jacob, E. (2010). Dominating clasp of the financial sector revealed by partial correlation analysis of the stock market. PLoS one, 5, e15032.

Examples

## Not run: 
neuralarray <- convertConnBrainMat()

dependencyneuralarray <- depna(neuralarray)

## End(Not run)

AlexChristensen/NetworkToolbox documentation built on March 6, 2023, 5:08 p.m.