Create interactive visualization charts to draw data in three dimensional graphs. The graphs can be included in Shiny apps and R markdown documents, or viewed from the R console and 'RStudio' Viewer. Based on the 'vis.js' Graph3d module and the 'htmlwidgets' R package.

Build graphs for landscape genetics analysis. This set of 
	functions can be used to import and convert spatial and genetic data 
	initially in different formats, import landscape graphs created with 
	'GRAPHAB' software (Foltete et al., 2012) <doi:10.1016/j.envsoft.2012.07.002>, 
	make diagnosis plots of isolation by distance relationships in order to 
	choose how to build genetic graphs, create graphs with a large range of 
	pruning methods, weight their links with several genetic distances, plot 
	and analyse graphs,	compare them with other graphs. It uses functions from 
	other packages such as 'adegenet' 
	(Jombart, 2008) <doi:10.1093/bioinformatics/btn129> and 'igraph' (Csardi
	et Nepusz, 2006) <https://igraph.org/>. It also implements methods 
	commonly used in landscape genetics to create graphs, described by Dyer et 
	Nason (2004) <doi:10.1111/j.1365-294X.2004.02177.x> and Greenbaum et 
	Fefferman (2017) <doi:10.1111/mec.14059>, and to analyse distance data 
	(van Strien et al., 2015) <doi:10.1038/hdy.2014.62>.

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.