Graphical modelling by log-linear models (an interface from R to
CoCo)
Interface to CoCo from R: Graphical modelling on
contingency tables by log-linear models
Copyright (C) by Jens Henrik Badsberg, 1991-2009, non-profit
use and redistribution permitted
Andreas Bjorns Gade 21, 2. tv., 1428 Kobenhavn K, Denmark
2010-03-18 20:43:25 UTC; Jens Henrik Badsberg

Two functions to compute correspondence analysis and constrained correspondence analysis
and to make the associated graphical representation.

Interface to CoCoCg (CoCo with both discrete and
continuous data) from R.
2010-03-18 20:53:09 UTC; Jens Henrik Badsberg

Interactive and dynamic graphs for the CoCo objects
Interface between dynamicGraph and CoCo (objects)
R (>= 2.10.0), dynamicGraph (>= 0.2.0), MASS, methods, CoCo
2010-03-18 20:53:17 UTC; Jens Henrik Badsberg

Deisy Morselli Gysi, Tiago de Miranda Fragoso, Eivind Almaas and Katja Nowick.
Categorize links from multiple networks in 3 categories: Common links (alpha) specific links (gamma), and different links (beta). Also categorizes the links into sub-categories and groups. The package includes a visualization tool for the networks. More information about the methodology can be found at: Gysi et. al., 2018 <arXiv:1802.00828>.
data.table, igraph, magrittr, plyr, parallel, visNetwork,
reshape2, geometry, plot3D

Categorize links and nodes from multiple networks in 3 categories: Common links (alpha) specific links (gamma), and different links (beta). Also categorizes the links into sub-categories and groups. The package includes a visualization tool for the networks. More information about the methodology can be found at: Gysi et. al., 2018 <arXiv:1802.00828>.
data.table, igraph, magrittr, plyr, visNetwork

data.table, igraph, magrittr, plyr, visNetwork, reshape2

data.table, igraph, magrittr, plyr, parallel, visNetwork

person("Rune", "Matthiesen", email = "runem2009@gmail.com", role = c("aut", "cre"))
Calculates complete functional regulation analysis and visualize the results in a single heatmap. The provided example data is for biological data but the methodology can be used for large data sets to compare quantitative entities that can be grouped. For example, a store might divide entities into cloth, food, car products etc and want to see how sales changes in the groups after some event. The theoretical background for the calculations are provided in New insights into functional regulation in MS-based drug profiling, Ana Sofia Carvalho, Henrik Molina & Rune Matthiesen, Scientific Reports, <doi:10.1038/srep18826>.