The 'NetCoupler' algorithm identifies potential direct effects of 
    correlated, high-dimensional variables formed as a network with an external 
    variable. The external variable may act as the dependent/response variable 
    or as an independent/predictor variable to the network.

Implementation of the NetCutter algorithm described in
    Müller and Mancuso (2008) <doi:10.1371/journal.pone.0003178>. The package
    identifies co-occurring terms in a list of containers. For example, it
    may be used to detect genes that co-occur across genomes.

Implementation of discriminant analysis with network structures in predictors accommodated to do classification and prediction.

Empirical statistical analysis, visualization and simulation of
    diffusion and contagion processes on networks. The package implements algorithms
    for calculating network diffusion statistics such as transmission rate, hazard
    rates, exposure models, network threshold levels, infectiousness (contagion),
    and susceptibility. The package is inspired by work published in Valente,
    et al., (2015) <DOI:10.1016/j.socscimed.2015.10.001>; Valente (1995) <ISBN:
    9781881303213>, Myers (2000) <DOI:10.1086/303110>, Iyengar and others (2011)
    <DOI:10.1287/mksc.1100.0566>, Burt (1987) <DOI:10.1086/228667>; among others.

A set of functions providing the implementation of the network meta-analysis model with dose-response relationships, predicted values of the fitted model and dose-response plots in a frequentist way.

Social network analysis has become an essential tool in the study of complex systems. 'NetExplorer' allows to visualize and explore complex systems. It is based on 'd3js' library that brings 1) Graphical user interface;  2) Circular, linear, multilayer and force Layout; 3) Network live exploration and 4) SVG exportation.

A network-guided penalized regression framework that integrates network characteristics from Gaussian graphical models with partial penalization, accounting for both network structure (hubs and non-hubs) and clinical covariates in high-dimensional omics data, including transcriptomics and proteomics. The full methodological details can be found in our recent preprint by Ahn S and Oh EJ (2025) <doi:10.48550/arXiv.2505.22986>.