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Tools for Identifying Important Nodes in Networks
Includes assorted tools for network analysis. Bridge centrality; goldbricker; MDS, PCA, & eigenmodel network plotting.
Additional Layout Algorithms for Network Visualizations
Several new layout algorithms to visualize networks are provided which are not part of 'igraph'.
Most are based on the concept of stress majorization by Gansner et al. (2004)
Visualization and Analysis Tools for Neural Networks
Visualization and analysis tools to aid in the interpretation of neural network models. Functions are available for plotting, quantifying variable importance, conducting a sensitivity analysis, and obtaining a simple list of model weights.
Similarity Network Fusion
Similarity Network Fusion takes multiple views of a network and fuses them together to construct an overall status matrix. The input to our algorithm can be feature vectors, pairwise distances, or pairwise similarities. The learned status matrix can then be used for retrieval, clustering, and classification.
Siena - Simulation Investigation for Empirical Network Analysis
The main purpose of this package is to perform simulation-based
estimation of stochastic actor-oriented models for longitudinal network
data collected as panel data. Dependent variables can be single or
multivariate networks, which can be directed, non-directed, or two-mode;
and associated actor variables.
There are also functions for testing parameters and checking goodness of fit.
An overview of these models is given in Snijders (2017),
Bayesian Regularization for Feed-Forward Neural Networks
Bayesian regularization for feed-forward neural networks.
Latent Position and Cluster Models for Statistical Networks
Fit and simulate latent position and cluster models for statistical networks. See Krivitsky and Handcock (2008)
Data Analysis for IP Addresses and Networks
Classes and functions for working with IP (Internet Protocol) addresses and networks, inspired by the Python 'ipaddress' module. Offers full support for both IPv4 and IPv6 (Internet Protocol versions 4 and 6) address spaces. It is specifically designed to work well with the 'tidyverse'.
R Interface to 'Keras'
Interface to 'Keras' < https://keras.io>, a high-level neural networks 'API'. 'Keras' was developed with a focus on enabling fast experimentation, supports both convolution based networks and recurrent networks (as well as combinations of the two), and runs seamlessly on both 'CPU' and 'GPU' devices.
An Simplified Implementation of the 'network' Package Functionality
An implementation of some of the core 'network' package functionality based on a simplified data structure that is faster in many research applications. This package is designed for back-end use in the 'statnet' family of packages, including 'EpiModel'. Support is provided for binary and weighted, directed and undirected, bipartite and unipartite networks; no current support for multigraphs, hypergraphs, or loops.