Methods and Measures for Semantic Network Analysis

Implements several functions for the analysis of semantic networks including different network estimation algorithms, partial node bootstrapping (Kenett, Anaki, & Faust, 2014 ), random walk simulation, and a function to compute global network measures. Significance tests, including permutation testing, and plotting features are also implemented.

Psychological Network Analysis and Psychometric Insights

Estimates psychological networks by group for research in the social sciences and complements them with psychometric modeling. It summarises centrality and bridge centrality indices, evaluates the stability of edges and centrality from bootstrap results, refines items by the stability of exploratory graph analysis (EGA), and plots networks with the variance explained of each node. It also estimates an a priori sample size for EGA through Monte Carlo simulation and includes an interactive application to plan the sample size of a network study. Methods follow Epskamp, Borsboom and Fried (2018) and Golino and Epskamp (2017) .

'RStudio' Addin for Network Analysis and Visualization

'RStudio' addin which provides a GUI to visualize and analyse networks. After finishing a session, the code to produce the plot is inserted in the current script. Alternatively, the function SNAhelperGadget() can be used directly from the console. Additional addins include the Netreader() for reading network files, Netbuilder() to create small networks via point and click, and the Componentlayouter() to layout networks with many components manually.

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'.

Network Analysis for Input-Output Tables

Network functionalities specialized for data generated from input-output tables.

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) .