Consensus Clustering using Multiple Algorithms and Parameters
c(person("Dr. T. Ian Simpson",
role = c("aut", "cre", "cph"),
comment = c(ORCID = "0000-0003-0495-7187"),
email = "ian.simpson@ed.ac.uk")
)
Functions for calculation of robustness measures for clusters and cluster membership based on generating consensus matrices from bootstrapped clustering experiments in which a random proportion of rows of the data set are used in each individual clustering. This allows the user to prioritise clusters and the members of clusters based on their consistency in this regime. The functions allow the user to select several algorithms to use in the re-sampling scheme and with any of the parameters that the algorithm would normally take. See Simpson, T. I., Armstrong, J. D. & Jarman, A. P. (2010) <doi:10.1186/1471-2105-11-590> and Monti, S., Tamayo, P., Mesirov, J. & Golub, T. (2003) <doi:10.1023/a:1023949509487>.
Dr. T. Ian Simpson [aut, cre, cph]
(<https://orcid.org/0000-0003-0495-7187>)

Bernard Desgraupes (University of Paris Ouest - Lab Modal'X)

random cluster generation (with specified degree of separation)
Weiliang Qiu <stwxq@channing.harvard.edu>,
Harry Joe <harry@stat.ubc.ca>.
The package contains functions for generating random
clusters, generating random covariance/correlation matrices,
calculating a separation index (data and population version)
for pairs of clusters or cluster distributions, and
1-D and 2-D projection plots to visualize clusters.
The package also contains a function to generate random
clusters based on factorial designs with factors such as
degree of separation, number of clusters,
number of variables, number of noisy variables.

Weiliang Qiu <stwxq@channing.harvard.edu>, Harry Joe
<harry@stat.ubc.ca>.
The package contains functions for generating random
clusters, generating random covariance/correlation matrices,
calculating a separation index (data and population version)
for pairs of clusters or cluster distributions, and 1-D and 2-D
projection plots to visualize clusters.  The package also
contains a function to generate random clusters based on
factorial designs with factors such as degree of separation,
number of clusters, number of variables, number of noisy
variables.