Gene Set Networking Analysis Package

Create networks of gene sets, infer clusters of functionally-related gene sets based on similarity statistics, and visualize the results. This package simplifies and accelerates interpretation of pathways analysis data sets. It is designed to work in tandem with standard pathways analysis methods, such as the 'GSEA' program (Gene Set Enrichment Analysis), CERNO (Coincident Extreme Ranks in Numerical Observations, implemented in the 'tmod' package) and others. Inputs to 'GSNA' are the outputs of pathways analysis methods: a list of gene sets (or "modules"), pathways or GO-terms with associated p-values. Since pathways analysis methods may be used to analyze many different types of data including transcriptomic, epigenetic, and high-throughput screen data sets, the 'GSNA' pipeline is applicable to these data as well. The use of 'GSNA' has been described in the following papers: Collins DR, Urbach JM, Racenet ZJ, Arshad U, Power KA, Newman RM, et al. (2021) , Collins DR, Hitschfel J, Urbach JM, Mylvaganam GH, Ly NL, Arshad U, et al. (2023) .

Large-Scale Social Network Analysis

We present an implementation of the algorithms required to simulate large-scale social networks and retrieve their most relevant metrics. Details can be found in the accompanying scientific paper on the Journal of Statistical Software, .

Integrative Differential Network Analysis in Genomics

Fits covariate dependent partial correlation matrices for integrative models to identify differential networks between two groups. The methods are described in Class et. al., (2018) and Ha et. al., (2015) .

Deciphering Central Informative Nodes in Network Analysis

Computing, comparing, and demonstrating top informative centrality measures within a network. "CINNA: an R/CRAN package to decipher Central Informative Nodes in Network Analysis" provides a comprehensive overview of the package functionality Ashtiani et al. (2018) .

Differential Network Analysis using Gene Pathways

Integrates pathway information into the differential network analysis of two gene expression datasets as described in Grimes, Potter, and Datta (2019) . Provides summary functions to break down the results at the pathway, gene, or individual connection level. The differential networks for each pathway of interest can be plotted, and the visualization will highlight any differentially expressed genes and all of the gene-gene associations that are significantly differentially connected.

Graph/Network Analysis Based on L1 Centrality

Analyze graph/network data using L1 centrality and prestige. Functions for deriving global, local, and group L1 centrality/prestige are provided. Routines for visual inspection of a graph/network are also provided. Details are in Kang and Oh (2025a) , Kang and Oh (2025b) , and Kang (2025) .

Structural Equation Modeling and Confirmatory Network Analysis

Multi-group (dynamical) structural equation models in combination with confirmatory network models from cross-sectional, time-series and panel data . Allows for confirmatory testing and fit as well as exploratory model search.

Tidy Geospatial Networks

Provides a tidy approach to spatial network analysis, in the form of classes and functions that enable a seamless interaction between the network analysis package 'tidygraph' and the spatial analysis package 'sf'.

Dynamic Extensions for Network Objects

Simple interface routines to facilitate the handling of network objects with complex intertemporal data. This is a part of the "statnet" suite of packages for network analysis.

Sparse Multiple Canonical Correlation Network Analysis Tool

A canonical correlation based framework (SmCCNet) designed for the construction of phenotype-specific multi-omics networks. This framework adeptly integrates single or multiple omics data types along with a quantitative or binary phenotype of interest. It offers a streamlined setup process that can be tailored manually or configured automatically, ensuring a flexible and user-friendly experience.