Fast, Robust, and Outlier Resistant Hierarchical Clustering
c(
person("Marek", "Gagolewski",
role = c("aut", "cre", "cph"),
email = "marek@gagolewski.com",
comment = c(ORCID = "0000-0003-0637-6028")),
person("Maciej", "Bartoszuk", role = "aut",
email = "bartoszuk@rexamine.com",
comment = c(ORCID = "0000-0001-6088-8273")),
person("Anna", "Cena", role = "aut",
email = "cena@rexamine.com",
comment = c(ORCID = "0000-0001-8697-5383"))
)
Includes the reference implementation of Genie - a hierarchical
clustering algorithm that links two point groups in such a way that
an inequity measure (namely, the Gini index) of the cluster sizes
does not significantly increase above a given threshold.
This method most often outperforms many other data segmentation approaches
in terms of clustering quality as tested on a wide range of benchmark
datasets. At the same time, Genie retains the high speed of the single
linkage approach, therefore it is also suitable for analysing larger data sets.
For more details see (Gagolewski et al. 2016 <DOI:10.1016/j.ins.2016.05.003>).
For an even faster and more feature-rich implementation, including,
amongst others, noise point detection, see the 'genieclust' package.

Project GENIE BioPharma Collaborative Data Processing Pipeline
c(
person(given = "Jessica A.",
family = "Lavery",
role = c("aut", "cre"),
email = "laveryj@mskcc.org",
comment = c(ORCID = "0000-0002-2746-5647")),
person(given = "Michael A.",
family = "Curry",
role = "aut",
comment = c(ORCID = "0000-0002-0261-4044")),
person(given = "Samantha",
family = "Brown",
role = "aut",
comment = c(ORCID = "0000-0001-5352-974X")),
person(given = "Karissa",
family = "Whiting",
role = "aut",
comment = c(ORCID = "0000-0002-4683-1868")),
person(given = "Axel",
family = "Martin",
role = "aut"),
person(given = "Daniel D.",
family = "Sjoberg",
role = c("ctb"),
email = "danield.sjoberg@gmail.com",
comment = c(ORCID = "0000-0003-0862-2018")))
The American Association Research (AACR) Project
Genomics Evidence Neoplasia Information Exchange (GENIE) BioPharma
Collaborative represents a multi-year, multi-institution effort
to build a pan-cancer repository of linked clinico-genomic data.
The genomic and clinical data are provided in multiple releases (separate
releases for each cancer cohort with updates following data corrections),
which are stored on the data sharing platform
'Synapse' <https://www.synapse.org/>.
The 'genieBPC' package provides a seamless way to obtain the
data corresponding to each release from 'Synapse' and to prepare
datasets for analysis.
Jessica A. Lavery [aut, cre] (<https://orcid.org/0000-0002-2746-5647>),
Michael A. Curry [aut] (<https://orcid.org/0000-0002-0261-4044>),
Samantha Brown [aut] (<https://orcid.org/0000-0001-5352-974X>),
Karissa Whiting [aut] (<https://orcid.org/0000-0002-4683-1868>),
Axel Martin [aut],
Daniel D. Sjoberg [ctb] (<https://orcid.org/0000-0003-0862-2018>)