The data science of networks is a rapidly developing field with myriad applications. In
neuroscience, the brain is commonly modeled as a connectome, a network of nodes …

Scientists have developed hundreds of techniques to measure the interactions between
pairs of processes in complex systems, but these computational methods—from …

This article presents dcor, an open-source Python package dedicated to distance correlation
and other statistics related to energy distance. These energy statistics include distances …

Replicability, the ability to replicate scientific findings, is a prerequisite for scientific discovery
and clinical utility. Troublingly, we are in the midst of a replicability crisis. A key to …

Causal inference studies whether the presence of a variable influences an observed
outcome. As measured by quantities such as the" average treatment effect," this paradigm is …

Distance correlation is a novel class of multivariate dependence measure, taking positive
values between 0 and 1, and applicable to random vectors of not necessarily equal arbitrary …

The full morphology of single neurons is indispensable for understanding cell types, the
basic building blocks in brains. Projecting trajectories are critical to extracting biologically …

The K-sample testing problem involves determining whether K groups of data points are
each drawn from the same distribution. Analysis of variance is arguably the most classical …

Using brain atlases to localize regions of interest is a required for making neuroscientifically
valid statistical inferences. These atlases, represented in volumetric or surface coordinate …

Testing whether two graphs come from the same distribution is of interest in many real‐world
scenarios, including brain network analysis. Under the random dot product graph model, the …