Set of functions for step-wise generation of (weighted) graphs. Aimed for research in the field of single- and multi-objective combinatorial optimization. Graphs are generated adding nodes, edges and weights. Each step may be repeated multiple times with different predefined and custom generators resulting in high flexibility regarding the graph topology and structure of edge weights.

A 'sparklyr' <https://spark.rstudio.com/> extension that provides an R 
  interface for 'GraphFrames' <https://graphframes.github.io/>. 'GraphFrames' is a package
  for 'Apache Spark' that provides a DataFrame-based API for working with graphs. Functionality
  includes motif finding and common graph algorithms, such as PageRank and Breadth-first
  search.

Provides a quick and easy access to the 'GraphHopper' Directions API.
  'GraphHopper' <https://www.graphhopper.com/> itself is a routing engine based on 'OpenStreetMap' data.
  API responses can be converted to simple feature (sf) objects in a convenient way.

Statistical methodology for sparse multivariate extreme value models. Methods are
             provided for exact simulation and statistical inference for multivariate Pareto distributions
             on graphical structures as described in the paper 'Graphical Models for Extremes' by
             Engelke and Hitz (2020) <doi:10.1111/rssb.12355>.

Estimates within and between time point interactions in experience sampling data, using the Graphical vector autoregression model in combination with regularization. See also Epskamp, Waldorp, Mottus & Borsboom (2018) <doi:10.1080/00273171.2018.1454823>.

A fast C++ implementation for computing various graph kernels including (1) simple kernels between vertex and/or edge label histograms, (2) graphlet kernels, (3) random walk kernels (popular baselines), and (4) the Weisfeiler-Lehman graph kernel (state-of-the-art).

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) <doi:10.1007/978-3-540-31843-9_25>. 
    Some more specific algorithms allow the user to emphasize hidden group structures in networks or focus on specific nodes.