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.

Fit, Simulate and Diagnose Exponential-Family Models for Multiple or Multilayer Networks

A set of extensions for the 'ergm' package to fit multilayer/multiplex/multirelational networks and samples of multiple networks. 'ergm.multi' is a part of the Statnet suite of packages for network analysis. See Krivitsky, Koehly, and Marcum (2020) and Krivitsky, Coletti, and Hens (2023) .

Fit, Simulate and Diagnose Exponential-Family Models for Networks with Count Edges

A set of extensions for the 'ergm' package to fit weighted networks whose edge weights are counts. See Krivitsky (2012) and Krivitsky, Hunter, Morris, and Klumb (2023) .

Algebraic Tools for the Analysis of Multiple Social Networks

Algebraic procedures for analyses of multiple social networks are provided with this package as described in Ostoic (2020) . 'multiplex' supports the creation and analysis of multiplex, multimode, and multilevel network data in various formats. It combines methods based on partially ordered semigroups, decomposition procedures, semiring structures, and relational bundles for multivariate network analysis. It also provides an algebraic approach to affiliation networks using Galois derivations, with visualization options.

Animal Social Network Inference and Permutations for Ecologists

Implements several tools that are used in animal social network analysis, as described in Whitehead (2007) Analyzing Animal Societies and Farine & Whitehead (2015) . In particular, this package provides the tools to infer groups and generate networks from observation data, perform permutation tests on the data, calculate lagged association rates, and performed multiple regression analysis on social network data.

Dense Neural Networks for Tabular Classification and Regression

Provides dense feed-forward neural network models for tabular regression and classification using 'torch'. The package supports modern extensions around dense neural network blocks, including dropout, batch normalization, residual connections, gated blocks, and optional input projection.

Extension to 'spatstat' for Large Datasets on a Linear Network

Extension to the 'spatstat' family of packages, for analysing large datasets of spatial points on a network. The geometrically- corrected K function is computed using a memory-efficient tree-based algorithm described by Rakshit, Baddeley and Nair (2019).

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.

Network Sparsification

Network sparsification with a variety of novel and known network sparsification techniques. All network sparsification techniques reduce the number of edges, not the number of nodes. Network sparsification is sometimes referred to as network dimensionality reduction. This package is based on the work of Spielman, D., Srivastava, N. (2009). Koutis I., Levin, A., Peng, R. (2013). Toivonen, H., Mahler, S., Zhou, F. (2010). Foti, N., Hughes, J., Rockmore, D. (2011).

Integration Network

It constructs a Consensus Network which identifies the general information of all the layers and Specific Networks for each layer with the information present only in that layer and not in all the others.The method is described in Policastro et al. (2024) "INet for network integration" .