The goal of this repo is to simulate Gaussian data with a covariance structure encoded by a (currently, unweighted and undirected) network. This repo is still in-progress and has not been tested comprehensively yet.
Given an input network, we want:
- If variables are connected in the input network, they should be correlated in the sample data.
- Variables with the same sign in the input network should be correlated in the same direction once data is sampled.
TODO: write up explanation of why setting correlation matrix directly doesn't work, make some visualizations, etc.