Hypothesis Testing for Populations of Brain Networks

Non-parametric hypothesis testing for populations of brain networks represented as graphs, following the L1-distance ANOVA framework of Fraiman and Fraiman (2018) . The package builds on this nonparametric graph-comparison framework, extending it with procedures for edge-level inference and identification of the specific connections driving group differences. In particular, it provides utilities to compute central (mean) graphs, pairwise Manhattan distances between adjacency matrices, the group test statistic T and its permutation p-value, and a fast permutation procedure to identify the critical edges that drive between-group differences. Helper functions to generate synthetic community-structured graphs and to visualise brain networks with communities are also included.