MergeKmeans 0.2.0

Robustness (pre-release stress campaign; see validation/robustness/REPORT.md)

Univariate data now default to single linkage: the components of 1-D clusters form a chain that Ward’s linkage cuts unreliably.
Unconverged Hartigan-Wong K-means solutions (Quick-TRANSfer cap at large n) are polished with Lloyd iterations so that the stored components are consistent with the stored centers and predict() reproduces them.
Diverging classification EM for the heteroscedastic variants (an instability inherent in their implied mixing proportions) now returns the last stable iteration with a warning instead of failing with an internal error; converged CEM fits return exactly the parameters that generated the labels, restoring the predict() round-trip.
G = "auto" is now computed from the single-linkage merge sequence of the overlap matrix regardless of the fitting linkage (the previous Ward-height gaps were unreliable); its limits (range 2:(K-1), spurious suggestions on structureless data) are documented.
Fixed: variant = "HeEC" crashed on univariate data; plot(fit, what = "tree") failed for K = 2; Inf values slipped past validation into cryptic downstream errors (MergeKmeans, predict, chooseK); overlap_map() crashed on missing values; predict() silently propagated NA.
kmeans++ seeding subsamples very large datasets (n > 1e5), removing the dominant cost of the n = 1e6, large-K regime.
New documentation: linkage guidance (Ward vs unequal cluster sizes and 1-D chains), contamination tolerance, heavy-tailed vs skewed clusters.

init = "kmeans++" is the new default initialization of the K-means restarts (Arthur & Vassilvitskii, 2007), stabilizing the large-K component solutions on which the merging is built; init = "random" restores the previous behavior.
start argument: MergeKmeans() can now merge a partition computed by any external engine (a kmeans object, a ClusterR-style list, or a bare label vector), skipping its own K-means step entirely. This removes the single-threaded stats::kmeans ceiling for very large datasets.
G = "auto" cuts the merge tree at the largest gap in merge heights (also available in recut()); when no G/omega.star is supplied the fit-time message now reports the suggested G.
K = NULL (new default) selects the number of components automatically via chooseK().
chooseK() now reports and marks a recommended K (one grid step beyond the detected elbow) and gained a cores argument.
cores argument forks the K-means restarts via the parallel package (POSIX systems).
broom tidiers: tidy(), glance(), and augment() methods are registered when broom is loaded.

Variant names are matched case-insensitively and accept plain-language aliases (“spherical”, “elliptical”, “spherical-unequal”, “elliptical-unequal”).
omega.star with Ward’s linkage is now an error (it previously warned and returned a degenerate unmerged partition).
A warning is emitted when components contain single observations (K too large relative to n).
Documentation: scaling guidance, a “when to use what” discussion (including the recommendation to prefer model-based clustering for genuinely overlapping Gaussian ellipsoids), and a recipe for merging mini-batch K-means solutions of massive datasets.

Initial version: DEMP-K merging of K-means solutions (Melnykov & Michael, 2020) with the four K-means variants (HoSC, HoEC, HeSC, HeEC), exact pairwise-overlap computations, single/Ward/average/complete linkages, the overlap map display, recut(), chooseK(), pairwise_overlap(), and print/summary/plot/predict methods.
All overlap computations validated against MixSim::overlap() and by independent Monte Carlo simulation; the paper’s illustrative examples and pen-digits application reproduced.