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Many Ways to Make, Modify, Mark, and Map Myriad Networks
A set of tools for making, modifying, marking, and mapping many different types of networks. All functions operate with matrices, edge lists, and 'igraph', 'network', and 'tidygraph' objects, and on one-mode, two-mode (bipartite), and sometimes three-mode networks. The package includes functions for importing and exporting, creating and generating networks, modifying networks and node and tie attributes, and describing and visualizing networks with sensible defaults.
Spatial Analysis on Network
Perform spatial analysis on network.
Implement several methods for spatial analysis on network: Network Kernel Density estimation,
building of spatial matrices based on network distance ('listw' objects from 'spdep' package), K functions estimation
for point pattern analysis on network, k nearest neighbours on network, reachable area calculation, and graph generation
References: Okabe et al (2019)
Computing Weighted Topological Overlaps (wTO) & Consensus wTO Network
Computes the Weighted Topological Overlap with positive and negative signs (wTO) networks given a data frame containing the mRNA count/ expression/ abundance per sample, and a vector containing the interested nodes of interaction (a subset of the elements of the full data frame). It also computes the cut-off threshold or p-value based on the individuals bootstrap or the values reshuffle per individual. It also allows the construction of a consensus network, based on multiple wTO networks. The package includes a visualization tool for the networks. More about the methodology can be found at
Network-Based Clustering
Network-based clustering using a Bayesian network mixture model with optional covariate adjustment.
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.
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)
Mobility Network Analysis
Implements the method to analyse weighted mobility networks or distribution networks as outlined in:
Block, P., Stadtfeld, C., & Robins, G. (2022)
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.
Statistically Validated Networks
Determines networks of significant synchronization between the discrete states of nodes; see Tumminello et al