Generative Neural Networks

Tools to set up, train, store, load, investigate and analyze generative neural networks. In particular, functionality for generative moment matching networks is provided.

'Gephi' Network Visualization

Implements key features of 'Gephi' for network visualization, including 'ForceAtlas2' (with LinLog mode), network scaling, and network rotations. It also includes easy network visualization tools such as edge and node color assignment for recreating 'Gephi'-style graphs in R. The package references layout algorithms developed by Jacomy, M., Venturini T., Heymann S., and Bastian M. (2014) and Noack, A. (2009) .

Statistical Comparison of Networks

A permutation-based hypothesis test for statistical comparison of two networks based on the invariance measures of the R package 'NetworkComparisonTest' by van Borkulo et al. (2022), : network structure invariance, global strength invariance, edge invariance, and various centrality measures. Edgelists from dependent or independent samples are used as input. These edgelists are generated from concept maps and summed into two comparable group networks. The networks can be directed or undirected.

Multiplex Network Analysis

Interactions between different biological entities are crucial for the function of biological systems. In such networks, nodes represent biological elements, such as genes, proteins and microbes, and their interactions can be defined by edges, which can be either binary or weighted. The dysregulation of these networks can be associated with different clinical conditions such as diseases and response to treatments. However, such variations often occur locally and do not concern the whole network. To capture local variations of such networks, we propose multiplex network differential analysis (MNDA). MNDA allows to quantify the variations in the local neighborhood of each node (e.g. gene) between the two given clinical states, and to test for statistical significance of such variation. Yousefi et al. (2023) .

Draw Network with Data

Extends the 'ggplot2' plotting system to support network visualization. Inspired by the 'Method 1' in 'ggtree' (G Yu (2018) ), 'ggtangle' is designed to work with network associated data.

NETwork COMparison Inference

Infer system functioning with empirical NETwork COMparisons. These methods are part of a growing paradigm in network science that uses relative comparisons of networks to infer mechanistic classifications and predict systemic interventions. They have been developed and applied in Langendorf and Burgess (2021) , Langendorf (2020) , and Langendorf and Goldberg (2019) .

Distance Measures for Networks

Network is a prevalent form of data structure in many fields. As an object of analysis, many distance or metric measures have been proposed to define the concept of similarity between two networks. We provide a number of distance measures for networks. See Jurman et al (2011) for an overview on spectral class of inter-graph distance measures.

Mobility Network Analysis

Implements the method to analyse weighted mobility networks or distribution networks as outlined in: Block, P., Stadtfeld, C., & Robins, G. (2022) . The purpose of the model is to analyse the structure of mobility, incorporating exogenous predictors pertaining to individuals and locations known from classical mobility analyses, as well as modelling emergent mobility patterns akin to structural patterns known from the statistical analysis of social networks.

Network of Differential Equations

Simulates a network of ordinary differential equations of order two. The package provides an easy interface to construct networks. In addition you are able to define different external triggers to manipulate the trajectory. The method is described by Surmann, Ligges, and Weihs (2014) .

Response Item Networks

Contains various tools to perform and visualize Response Item Networks ('ResIN's'). 'ResIN' binarizes ordered-categorical and qualitative response choices from (survey) data, calculates pairwise associations and maps the location of each item response as a node in a force-directed network. Please refer to < https://www.resinmethod.net/> for more details.