c(person("Arash", "Shahsavari", email="as3006@cam.ac.uk",
role = c("aut", "cre")), person("Irina", "Mohorianu",
email="iim22@cam.ac.uk", role="aut"))
A set of tools for evaluating clustering similarity across methods
and method stability using element-centric clustering comparison (Gates et
al. (2019) <doi:10.1038/s41598-019-44892-y>). Additionally, this package
enables data-wide assessment of clustering robustness using proportion of
ambiguously clustered pairs (Senbabaoglu et al. (2014)
<doi:10.1038/srep06207>), which can be used to infer the optimal number of
clusters in the data.
ggplot2, dplyr, fastcluster, rlang, Matrix, igraph, magrittr,
Rcpp, methods, stats
knitr, rmarkdown, e1071, dbscan, dendextend, Seurat
Arash Shahsavari [aut, cre],
Irina Mohorianu [aut]

c(person("Arash", "Shahsavari", email="as3006@cam.ac.uk",
role = c("aut", "cre")), person("Andi", "Munteanu",
email="am3019@cam.ac.uk", role="aut"), person("Irina", "Mohorianu",
email="iim22@cam.ac.uk", role="aut"))
A set of tools for evaluating clustering robustness using
proportion of ambiguously clustered pairs (Senbabaoglu et al. (2014)
<doi:10.1038/srep06207>), as well as similarity across methods
and method stability using element-centric clustering comparison (Gates et
al. (2019) <doi:10.1038/s41598-019-44892-y>). Additionally, this package
enables stability-based parameter assessment for graph-based clustering
pipelines typical in single-cell data analysis.
ggplot2, dplyr, fastcluster, rlang, Matrix, igraph, magrittr,
Rcpp, methods, stats, foreach, doParallel, irlba, progress,
reshape2, stringr, uwot
knitr, rmarkdown, e1071, dbscan, dendextend, Seurat, readr,
patchwork
Arash Shahsavari [aut, cre],
Andi Munteanu [aut],
Irina Mohorianu [aut]

c(person("Fabien", "Llobell", email = "fabien.llobell@oniris-nantes.fr", role = c("aut","cre"), comment="Oniris/XLSTAT"), person("Evelyne", "Vigneau", email = "evelyne.vigneau@oniris-nantes.fr", role = "ctb", comment="Oniris"),  person("Veronique", "Cariou", email = "veronique.cariou@oniris-nantes.fr", role = "ctb", comment="Oniris"), person("El Mostafa", "Qannari", email = "elmostafa.qannari@oniris-nantes.fr", role = "ctb", comment="Oniris"))
Hierarchical and partitioning algorithms of blocks of variables. The partitioning algorithm includes an option called noise cluster to set aside atypical blocks of variables. The CLUSTATIS method (for quantitative blocks) (Llobell, Cariou, Vigneau, Labenne & Qannari (2018) <doi:10.1016/j.foodqual.2018.05.013>)  and the CLUSCATA method (for Check-All-That-Apply data) (Llobell, Cariou, Vigneau, Labenne & Qannari (2019) <doi:10.1016/j.foodqual.2018.09.006>) are the core of this package.
Fabien Llobell [aut, cre] (Oniris/XLSTAT),
Evelyne Vigneau [ctb] (Oniris),
Veronique Cariou [ctb] (Oniris),
El Mostafa Qannari [ctb] (Oniris)