MixMashNet: An R Package for Single and Multilayer Networks
Maria De Martino, Federico Triolo, Adrien Perigord, Alice Margherita Ornago, Davide Liborio Vetrano, Caterina Gregorio
TL;DR
MixMashNet addresses the need for a unified framework to estimate and interpret single and multilayer networks with mixed data types. It leverages Mixed Graphical Models (MGMs) to model conditional dependencies, supports topology constraints via masked MGMs, and provides comprehensive bootstrap-based uncertainty quantification for edges, node indices, and community structure. The package extends standard centrality analyses with interlayer and bridging metrics, assesses community membership stability, and computes subject-level community scores, all within an integrated R workflow and interactive Shiny apps. This approach enables reliable, interpretable network analysis in high-dimensional, heterogeneous data settings with explicit multilayer topology, improving applicability in domains requiring cross-layer insights and robust inference.
Abstract
The R package MixMashNet provides an integrated framework for estimating and analyzing single and multilayer networks using Mixed Graphical Models (MGMs), accommodating continuous, count, and categorical variables. In the multilayer setting, layers may comprise different types and numbers of variables, and users can explicitly impose a predefined multilayer topology. Bootstrap procedures are implemented to derive confidence intervals for edge weights and node-level centrality indices. In addition, the package includes tools to assess the stability of node community membership and to compute community scores that summarize the latent dimensions identified through network clustering. MixMashNet also offers interactive Shiny applications to support exploration, visualization, and interpretation of the estimated networks.