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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.
Geometries to Plot Networks with 'ggplot2'
Geometries to plot network objects with 'ggplot2'.
Tidy Geospatial Networks
Provides a tidy approach to spatial network analysis, in the form of classes and functions that enable a seamless interaction between the network analysis package 'tidygraph' and the spatial analysis package 'sf'.
Visualising Bipartite Networks and Calculating Some (Ecological) Indices
Functions to visualise webs and calculate a series of indices commonly used to describe pattern in (ecological) webs. It focuses on webs consisting of only two levels (bipartite), e.g. pollination webs or predator-prey-webs. Visualisation is important to get an idea of what we are actually looking at, while the indices summarise different aspects of the web's topology.
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.
Bayesian Networks
Probability propagation in Bayesian networks, also known as graphical independence networks. Documentation
of the package is provided in vignettes included in the package and in
the paper by Højsgaard (2012,
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)
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.
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.