Dyadic Network Analysis

Contains functions for the MCMC simulation of (multilevel) dyadic network models j2 (Zijlstra, 2017, ) and p2 (Van Duijn, Snijders & Zijlstra, 2004, ), the multilevel p2 model (Zijlstra, Van Duijn & Snijders (2009) ), and the bidirectional (multilevel) counterpart of the the multilevel p2 model as described in Zijlstra, Van Duijn & Snijders (2009) , the (multilevel) b2 model.

Network Analysis of Immune Repertoire

Pipelines for studying the adaptive immune repertoire of T cells and B cells via network analysis based on receptor sequence similarity. Relate clinical outcomes to immune repertoires based on their network properties, or to particular clusters and clones within a repertoire. Yang et al. (2023) .

Tidy Temporal Network Analysis

Building, analysis and visualisation of temporal networks. Supports several formats of relational data: interval data with explicit start and end times, contact data of instantaneous events, threaded interactions such as forum or chat data, and co-presence logs. Every analysis returns a tidy data frame carrying proper vertex labels. Metrics cover time-varying centrality, graph-level structure, edge formation and dissolution, burstiness, time-respecting paths, reachability and mixing. Offers a wide variety of network visualisations and animations, as well as plots of temporal network metrics across time.

Heterogeneous Transition Network Analysis

Implements the Heterogeneous Transition Network Analysis (HTNA) method described by López-Pernas et al. (2026) . The method is an extension of transition network analysis (TNA) where actions or events belong to two or more distinct actor types (e.g. Human and AI), preserving the actor type partition on the resulting network. Provides a thin, focused API on top of the 'Nestimate' estimation engine and the 'cograph' rendering engine, so downstream bootstrap, permutation, reliability, centrality, and plotting functions treat each actor's codes as a distinct node group.

Network Analysis and Community Detection

Features tools for the network data analysis and community detection. Provides multiple methods for fitting, model selection and goodness-of-fit testing in degree-corrected stochastic blocks models. Most of the computations are fast and scalable for sparse networks, esp. for Poisson versions of the models. Implements the following: Amini, Chen, Bickel and Levina (2013) Bickel and Sarkar (2015) Lei (2016) Wang and Bickel (2017) Zhang and Amini (2020) Le and Levina (2022) .

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 (2025) and Nehler and Schultze (2026) . 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.

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.