Graph clustering using an agglomerative algorithm to maximize the
  integrated classification likelihood criterion and a mixture of stochastic
  block models. The method is described in the article "Model-based clustering 
  of multiple networks with a hierarchical algorithm" by 
  T. Rebafka (2022) <arXiv:2211.02314>.

A multi-platform user interface for drawing highly customizable graphs in R. It aims to be a valuable help to quickly draw publishable graphs without any knowledge of R commands. Six kinds of graph are available: histogram, box-and-whisker plot, bar plot, pie chart, curve and scatter plot.

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.

Computes marginal likelihood in Gaussian graphical models through a
  novel telescoping block decomposition of the precision matrix which allows
  estimation of model evidence. The top level function used to estimate marginal
  likelihood is called evidence(), which expects the prior name, data, and
  relevant prior specific parameters. This package also provides an MCMC prior
  sampler using the same underlying approach, implemented in prior_sampling(),
  which expects a prior name and prior specific parameters. Both functions also
  expect the number of burn-in iterations and the number of sampling iterations
  for the underlying MCMC sampler.