Gregory Warnes, with contributions from Gregor Gorjanc,
Friedrich Leisch, and Michael Man.

Classes and methods for handling genetic data. Includes
classes to represent genotypes and haplotypes at single markers
up to multiple markers on multiple chromosomes. Function
include allele frequencies, flagging homo/heterozygotes,
flagging carriers of certain alleles, estimating and testing
for Hardy-Weinberg disequilibrium, estimating and testing for
linkage disequilibrium, ...

A New, Fast, and Outlier Resistant Hierarchical Clustering
Algorithm
c(
person("Marek", "Gagolewski", role = c("aut", "cre"),
email = "gagolews@rexamine.com"),
person("Maciej", "Bartoszuk", role = "aut",
email = "bartoszuk@rexamine.com"),
person("Anna", "Cena", role = "aut",
email = "cena@rexamine.com")
)
A new hierarchical clustering linkage criterion:
the Genie algorithm links two clusters in such a way that a chosen
economic inequity measure (e.g., the Gini index) of the cluster
sizes does not increase drastically above a given threshold. Benchmarks
indicate a high practical usefulness of the introduced method:
it most often outperforms the Ward or average linkage in terms of
the clustering quality while retaining the single linkage speed.
Marek Gagolewski [aut, cre],
Maciej Bartoszuk [aut],
Anna Cena [aut]

A new hierarchical clustering linkage criterion:
the Genie algorithm links two clusters in such a way that a chosen
economic inequity measure (e.g., the Gini index) of the cluster
sizes does not increase drastically above a given threshold. Benchmarks
indicate a high practical usefulness of the introduced method:
it most often outperforms the Ward or average linkage in terms of
the clustering quality while retaining the single linkage speed,
see (Gagolewski et al. 2016 <DOI:10.1016/j.ins.2016.05.003>)
for more details.

A new hierarchical clustering linkage criterion:
the Genie algorithm links two clusters in such a way that a chosen
economic inequity measure (e.g., the Gini index) of the cluster
sizes does not increase drastically above a given threshold. Benchmarks
indicate a high practical usefulness of the introduced method:
it most often outperforms the Ward or average linkage in terms of
the clustering quality while retaining the single linkage speed,
see (Gagolewski et al. 2016a <DOI:10.1016/j.ins.2016.05.003>,
2016b <DOI:10.1007/978-3-319-45656-0_16>)
for more details.