gen.sample.labels

<p>whether the number of samples/class should be equal, with each
class having a prior of 1/K, or inequal, in which each class obtains a prior
of k/sum(K) for k=1:K. Defaults to <code>TRUE</code>.</p>

class.equal

<p>A helper function for simulating sample labels</p>

internal

Multiscale Graph Correlation (MGC) is a framework developed by Vogelstein et al. (2019) <DOI:10.7554/eLife.41690> that extends global correlation procedures to be multiscale; consequently, MGC tests typically require far fewer samples than existing methods for a wide variety of dependence structures and dimensionalities, while maintaining computational efficiency. Moreover, MGC provides a simple and elegant multiscale characterization of the potentially complex latent geometry underlying the relationship.

Eric Bridgeford

gen.sample.labels: A helper function for simulating sample labels

Description

Usage

Arguments