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.

Examples of Neural Networks

Implementations of several basic neural network concepts in R, as based on posts on \url{ http://qua.st/}.

Analyzing Ecological Networks

A collection of advanced tools, methods and models specifically designed for analyzing different types of ecological networks - especially antagonistic (food webs, host-parasite), mutualistic (plant-pollinator, plant-fungus, etc) and competitive networks, as well as their variability in time and space. Statistical models are developed to describe and understand the mechanisms that determine species interactions, and to decipher the organization of these ecological networks (Ohlmann et al. (2019) , Gonzalez et al. (2020) , Miele et al. (2021) , Botella et al (2021) ).

Neural Network Numerai

Interactively train neural networks on Numerai, < https://numer.ai/>, data. Generate tournament predictions and write them to a CSV.

Statistically Validated Networks

Determines networks of significant synchronization between the discrete states of nodes; see Tumminello et al .

Complex Network Generation

Providing a set of functions to easily generate and iterate complex networks. The functions can be used to generate realistic networks with a wide range of different clustering, density, and average path length. For more information consult research articles by Amiyaal Ilany and Erol Akcay (2016) and Ilany and Erol Akcay (2016) , which have inspired many methods in this package.

Network Diffusion Algorithms

Implementation of network diffusion algorithms such as heat diffusion or Markov random walks. Network diffusion algorithms generally spread information in the form of node weights along the edges of a graph to other nodes. These weights can for example be interpreted as temperature, an initial amount of water, the activation of neurons in the brain, or the location of a random surfer in the internet. The information (node weights) is iteratively propagated to other nodes until a equilibrium state or stop criterion occurs.