Description: Create correlation heatmaps from a numeric matrix. Ensembl Gene ID row names can be converted to Gene Symbols
using, e.g., BioMart. Optionally, data can be clustered and filtered by correlation, tree cutting and/or number
of missing values. Genes of interest can be highlighted in the plot and correlation significance be indicated by
asterisks encoding corresponding P-Values. Plot dimensions and label measures are adjusted automatically by default.
The plot features rely on the heatmap.n2() function in the 'heatmapFlex' package.
Imports: WGCNA, heatmapFlex, convertid (>= 0.1.3), methods, graphics,

Title: Fast and Flexible Core Subset Selection
Authors@R: c(person("Herman", "De Beukelaer", email = "herman.debeukelaer@gmail.com", role = c("aut", "cre")),
person("Guy", "Davenport", email = "daveneti@gmail.com", role = "aut"),
person("Veerle", "Fack", email = "veerle.fack@ugent.be", role = "ths"))
Description: Interface to the Core Hunter software for core subset selection.
Cores can be constructed based on genetic marker data, phenotypic traits,
a precomputed distance matrix, or any combination of these. Various measures
are included such as Modified Rogers' distance and Shannon's diversity index
(for genotypes) and Gower's distance (for phenotypes). Core Hunter can also
optimize a weighted combination of multiple measures, to bring the different
Imports: naturalsort (>= 0.1.2), methods
SystemRequirements: Java JRE 8 or higher
Maintainer: Herman De Beukelaer <herman.debeukelaer@gmail.com>

Title: Multi-Purpose Core Subset Selection
Description: Core Hunter is a tool to sample diverse, representative subsets from large germplasm
collections, with minimum redundancy. Such so-called core collections have applications in plant
breeding and genetic resource management in general. Core Hunter can construct cores based on
genetic marker data, phenotypic traits or precomputed distance matrices, optimizing one of many
provided evaluation measures depending on the precise purpose of the core (e.g. high diversity,
representativeness, or allelic richness). In addition, multiple measures can be simultaneously
optimized as part of a weighted index to bring the different perspectives closer together.
The Core Hunter library is implemented in Java 8 as an open source project (see