ade4, Claddis, geiger, geometry, geoscale, graphics,
grDevices, methods, mnormt, phangorn, phyclust, utils, vegan

A modular package for measuring disparity (multidimensional space occupancy). Disparity can be calculated from any matrix defining a multidimensional space. The package provides a set of implemented metrics to measure properties of the space and allows users to provide and test their own metrics (Guillerme (2018) <doi:10.1111/2041-210X.13022>). The package also provides functions for looking at disparity in a serial way (e.g. disparity through time - Guillerme and Cooper (2018) <doi:10.1111/pala.12364>) or per groups as well as visualising the results. Finally, this package provides several statistical tests for disparity analysis.
ade4, castor, Claddis, geiger, geometry, geoscale, graphics,
grDevices, methods, mnormt, phangorn, phyclust, utils, vegan

ade4, castor, Claddis, geiger, geometry, geoscale, graphics,
grDevices, methods, mnormt, parallel, phangorn, phyclust,
utils, vegan, scales

ade4, castor, Claddis, geometry, geoscale, graphics,
grDevices, MASS, methods, mnormt, parallel, phangorn, phyclust,
utils, vegan, scales,

A modular package for measuring disparity (multidimensional space occupancy). Disparity can be calculated from any matrix defining a multidimensional space. The package provides a set of implemented metrics to measure properties of the space and allows users to provide and test their own metrics. The package also provides functions for looking at disparity in a serial way (e.g. disparity through time) or per groups as well as visualising the results. Finally, this package provides several statistical tests for disparity analysis.
ade4, castor, Claddis, ellipse, geometry, graphics, grDevices,
MASS, methods, mnormt, parallel, phangorn, phyclust, utils,
vegan, scales,

Disparity Filter Algorithm of Weighted Network
Disparity filter is a network reduction algorithm to extract the backbone structure of both directed and undirected weighted networks. Disparity filter can reduce the network without destroying the multi-scale nature of the network. The algorithm has been developed by M. Angeles Serrano, Marian Boguna, and Alessandro Vespignani in Extracting the multiscale backbone of complex weighted networks, Proceedings of the national academy of sciences 106 (16).