c(person("Martin","Maechler", role = c("aut","cre"),
email="maechler@stat.math.ethz.ch", comment = c(ORCID = "0000-0002-8685-9910"))
,person("Peter", "Rousseeuw", role="aut", email="peter.rousseeuw@kuleuven.be",
comment = c("Fortran original", ORCID = "0000-0002-3807-5353"))
,person("Anja", "Struyf", role="aut", comment= "S original")
,person("Mia", "Hubert", role="aut", email= "Mia.Hubert@uia.ua.ac.be",
comment = c("S original", ORCID = "0000-0001-6398-4850"))
,person("Kurt", "Hornik", role=c("trl", "ctb"),
email="Kurt.Hornik@R-project.org",
comment=c("port to R; maintenance(1999-2000)", ORCID="0000-0003-4198-9911"))
,person("Matthias", "Studer", role="ctb")
,person("Pierre", "Roudier", role="ctb")
,person("Juan",   "Gonzalez", role="ctb")
,person("Kamil",  "Kozlowski", role="ctb")
,person("Erich",  "Schubert", role="ctb", comment = c("fastpam options for pam()",
ORCID = "0000-0001-9143-4880"))
,person("Keefe",  "Murphy", role="ctb", comment = "volume.ellipsoid({d >= 3})")
)
Martin Maechler [aut, cre] (<https://orcid.org/0000-0002-8685-9910>),
Peter Rousseeuw [aut] (Fortran original,
<https://orcid.org/0000-0002-3807-5353>),
Anja Struyf [aut] (S original),
Mia Hubert [aut] (S original, <https://orcid.org/0000-0001-6398-4850>),
Kurt Hornik [trl, ctb] (port to R; maintenance(1999-2000),
<https://orcid.org/0000-0003-4198-9911>),
Matthias Studer [ctb],
Pierre Roudier [ctb],
Juan Gonzalez [ctb],
Kamil Kozlowski [ctb],
Erich Schubert [ctb] (fastpam options for pam(),
<https://orcid.org/0000-0001-9143-4880>),
Keefe Murphy [ctb] (volume.ellipsoid({d >= 3}))

A collection of data sets for teaching cluster analysis.

Calculate the consensus clustering result from re-sampled
clustering experiments with the option of using multiple
algorithms and parameter
Dr. T. Ian Simpson, University of Edinburgh
Dr. T. Ian Simpson <ian.simpson@ed.ac.uk>
clusterCons is a package containing functions that
generate 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.