Missingness Alleviation for Network Analysis

Provides functionality for estimating cross-sectional network structures representing partial correlations while accounting for missing data. Networks are estimated via neighborhood selection or regularization, with model selection guided by information criteria. Missing data can be handled primarily via multiple imputation or a maximum likelihood-based approach, as demonstrated by Nehler and Schultze (2025a) and Nehler and Schultze (2025b) . Deletion-based approaches are also available but play a secondary role.

Multi-Layer Networks Analysis

Provides two general frameworks to generate a multi-layer network. This also provides several methods to reveal the embedding of both nodes and layers. The reference paper can be found from the URL mentioned below. Ting Li, Zhongyuan Lyu, Chenyu Ren, Dong Xia (2023) .

A Collection of Tools for Network Analysis

Provides a collection of network analytic (convenience) functions which are missing in other standard packages. This includes triad census with attributes , core-periphery models , and several graph generators. Most functions are build upon 'igraph'.

Network Analysis on the Norwegian Road Network

A collection of GIS (Geographic Information System) functions in R, created for use in Statistics Norway. The functions are primarily related to network analysis on the Norwegian road network.

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.

Statistical Network Analysis of Animal Social Networks

Obtain network structures from animal GPS telemetry observations and statistically analyse them to assess their adequacy for social network analysis. Methods include pre-network data permutations, bootstrapping techniques to obtain confidence intervals for global and node-level network metrics, and correlation and regression analysis of the local network metrics.

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 (Kenett & Austerweil, 2016 < http://alab.psych.wisc.edu/papers/files/Kenett16CreativityRW.pdf>), and a function to compute global network measures. Significance tests and plotting features are also implemented.

'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.

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.