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) .

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.

Road Network Projection

Project road network development based on an existing road network, target locations to be connected by roads and a cost surface. Road projection methods include minimum spanning tree with least cost path (Kruskal's algorithm (1956) ), least cost path (Dijkstra's algorithm (1959) ) or snapping. These road network projection methods are ideal for use with land cover change projection models.

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) .

Examples of Neural Networks

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

Neural Network Numerai

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

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) ).

Statistically Validated Networks

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