🤖 AI Summary
This study addresses the challenge of unified network modeling for heterogeneous variables—encompassing continuous, count, and categorical types—within arbitrarily structured multilayer topologies. Building upon mixed graphical models (MGM), the work proposes a flexible framework for both single-layer and multilayer network construction. Edge weights and centrality measures are accompanied by confidence intervals derived via bootstrap resampling, while community stability is assessed through network clustering. An integrated Shiny-based interface enables interactive visualization of results. Notably, this project delivers the first comprehensive R toolkit that simultaneously supports heterogeneous data, customizable interlayer architectures, uncertainty quantification, and community stability analysis, thereby substantially enhancing the interpretability and robustness of multilayer network modeling.
📝 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 quantify sampling uncertainty 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.