Neural Networks using the Stuttgart Neural Network Simulator (SNNS)

The Stuttgart Neural Network Simulator (SNNS) is a library containing many standard implementations of neural networks. This package wraps the SNNS functionality to make it available from within R. Using the 'RSNNS' low-level interface, all of the algorithmic functionality and flexibility of SNNS can be accessed. Furthermore, the package contains a convenient high-level interface, so that the most common neural network topologies and learning algorithms integrate seamlessly into R.

Fit, Simulate and Diagnose Exponential-Family Models for Networks

An integrated set of tools to analyze and simulate networks based on exponential-family random graph models (ERGMs). 'ergm' is a part of the Statnet suite of packages for network analysis. See Hunter, Handcock, Butts, Goodreau, and Morris (2008) and Krivitsky, Hunter, Morris, and Klumb (2023) .

A Collection of Network Data Sets for the 'igraph' Package

A small collection of various network data sets, to use with the 'igraph' package: the Enron email network, various food webs, interactions in the immunoglobulin protein, the karate club network, Koenigsberg's bridges, visuotactile brain areas of the macaque monkey, UK faculty friendship network, domestic US flights network, etc.

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.

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.

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.

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 .