Time Series Forecasting with Neural Networks

Automatic time series modelling with neural networks. Allows fully automatic, semi-manual or fully manual specification of networks. For details of the specification methodology see: (i) Crone and Kourentzes (2010) ; and (ii) Kourentzes et al. (2014) .

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.

Dynamic Extensions for Network Objects

Simple interface routines to facilitate the handling of network objects with complex intertemporal data. This is a part of the "statnet" suite of packages for network analysis.

Bayesian Regularization for Feed-Forward Neural Networks

Bayesian regularization for feed-forward neural networks.

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) . 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) .

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.