Initial CRAN release.
Provides seven fast k-means clustering algorithms behind a single, uniform interface, wrapping high-performance C++ implementations via 'Rcpp' and 'RcppEigen':
geo_kmeans() — Geometric-k-means, the bound-free method of Sharma et al.
(2026) doi:10.1007/s10994-025-06891-1.ball_kmeans() — Ball k-means++.lloyd_kmeans(), elkan_kmeans(), hamerly_kmeans(), annulus_kmeans(),
and exponion_kmeans().kmeans_dc() — dispatcher to select any of the above by name.centers accepts either the number of clusters or a matrix of initial
centroids (mirroring stats::kmeans()); initialisation can be "random" or
"sequential".
Returns a geokmeans object with the final centroids, per-point cluster
assignments, iteration count, and number of distance computations, along with
a print() method.
Random initialisation uses R's random number generator and is reproducible via
set.seed() or the optional seed argument (default NULL).
Safeguards for degenerate input: an informative error when more clusters are
requested than there are distinct observations, and optional removal of empty
clusters via drop_empty.
Ships two example datasets in inst/extdata (Breastcancer.csv and
CreditRisk.csv) and a "Getting started with geokmeans" vignette.