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) . Some more specific algorithms allow the user to emphasize hidden group structures in networks or focus on specific nodes.

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.

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.

Latent Position and Cluster Models for Statistical Networks

Fit and simulate latent position and cluster models for statistical networks. See Krivitsky and Handcock (2008) and Krivitsky, Handcock, Raftery, and Hoff (2009) .

Construction, Simulation and Analysis of Boolean Networks

Functions to reconstruct, generate, and simulate synchronous, asynchronous, probabilistic, and temporal Boolean networks. Provides also functions to analyze and visualize attractors in Boolean networks .

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.

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'.

Generalized and Classical Blockmodeling of Valued Networks

This is primarily meant as an implementation of generalized blockmodeling for valued networks. In addition, measures of similarity or dissimilarity based on structural equivalence and regular equivalence (REGE algorithms) can be computed and partitioned matrices can be plotted: Žiberna (2007), Žiberna (2008), Žiberna (2014).

Extension to 'ggplot2'

The R package 'ggplot2' is a plotting system based on the grammar of graphics. 'GGally' extends 'ggplot2' by adding several functions to reduce the complexity of combining geometric objects with transformed data. Some of these functions include a pairwise plot matrix, a two group pairwise plot matrix, a parallel coordinates plot, a survival plot, and several functions to plot networks.

Interactive 3D Scatter Plots, Networks and Globes

Create interactive 3D scatter plots, network plots, and globes using the 'three.js' visualization library (< https://threejs.org>).