klic: Kernel Learning Integrative Clustering

Kernel Learning Integrative Clustering (KLIC) is an algorithm that allows to combine multiple kernels, each representing a different measure of the similarity between a set of observations. The contribution of each kernel on the final clustering is weighted according to the amount of information carried by it. As well as providing the functions required to perform the kernel-based clustering, this package also allows the user to simply give the data as input: the kernels are then built using consensus clustering. Different strategies to choose the best number of clusters are also available. For further details please see Cabassi and Kirk (2019) <arXiv:1904.07701>.

Version: 1.0.2
Depends: R (≥ 3.5.0)
Imports: Matrix, cluster, gplots, wesanderson, coca, RColorBrewer, pheatmap, utils
Suggests: Rmosek, tikzDevice, mclust, grDevices, graphics, knitr
Published: 2020-04-03
Author: Alessandra Cabassi ORCID iD [aut, cre], Paul DW Kirk ORCID iD [ths], Mehmet Gonen ORCID iD [ctb]
Maintainer: Alessandra Cabassi <alessandra.cabassi at mrc-bsu.cam.ac.uk>
BugReports: http://github.com/acabassi/klic/issues
License: MIT + file LICENSE
URL: http://github.com/acabassi/klic
NeedsCompilation: no
SystemRequirements: MOSEK (http://www.mosek.com) and MOSEK license.
Citation: klic citation info
Materials: README
CRAN checks: klic results


Reference manual: klic.pdf
Vignettes: R package klic
Package source: klic_1.0.2.tar.gz
Windows binaries: r-prerelease: klic_1.0.2.zip, r-release: klic_1.0.2.zip, r-oldrel: klic_1.0.2.zip
macOS binaries: r-prerelease: klic_1.0.2.tgz, r-release: klic_1.0.2.tgz, r-oldrel: not available


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