Network Analysis of Dependencies of CRAN Packages

The dependencies of CRAN packages can be analysed in a network fashion. For each package we can obtain the packages that it depends, imports, suggests, etc. By iterating this procedure over a number of packages, we can build, visualise, and analyse the dependency network, enabling us to have a bird's-eye view of the CRAN ecosystem. One aspect of interest is the number of reverse dependencies of the packages, or equivalently the in-degree distribution of the dependency network. This can be fitted by the power law and/or an extreme value mixture distribution , of which functions are provided.

Co-Expression Differential Network Analysis

Categorize links and nodes from multiple networks in 3 categories: Common links (alpha) specific links (gamma), and different links (beta). Also categorizes the links into sub-categories and groups. The package includes a visualization tool for the networks. More information about the methodology can be found at: Gysi et. al., 2018 .

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) .

Visualization and Analysis Tools for Neural Networks

Visualization and analysis tools to aid in the interpretation of neural network models. Functions are available for plotting, quantifying variable importance, conducting a sensitivity analysis, and obtaining a simple list of model weights.

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) .

Dynamic, Probabilistic, and Higher-Order Network Analysis

Estimate, compare, and analyze dynamic and psychological networks using a unified interface. Provides transition network analysis estimation (transition, frequency, co-occurrence, attention-weighted) Saqr et al. (2025) , psychological network methods (correlation, partial correlation, 'graphical lasso', 'Ising') Saqr, Beck, and Lopez-Pernas (2024) , and higher-order network methods including higher-order networks, higher-order network embedding, hyper-path anomaly, and multi-order generative model. Supports bootstrap inference, permutation testing, split-half reliability, centrality stability analysis, mixed Markov models, multi-cluster multi-layer networks and clustering.

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.

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'.