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cec(..., split = TRUE) whenever every
attempt at the initial (pre-split) clustering failed to produce a valid
result (e.g. too few observations for the enforced card.min
floor, most commonly a subset of 3 or fewer points). The internal
split-mode code path dereferenced that failed result unconditionally
instead of treating it as another unsuccessful start, segfaulting the R
process. It now surfaces as the same catchable
"all starts failed" error already raised by the non-split
path in this situation.<cmath> in the src/models/*.h headers,
<functional> in variable_starter.h,
<iterator> in vec.h,
<limits> in init.cpp, and
<utility> in r_ext_ptr.h and
common.h.cec() now accepts an optional weights
argument: a numeric vector of non-negative values, one per observation.
Cluster means, covariances, and the returned $probability
field are all weight-aware. When weights = NULL (default),
behaviour is identical to previous versions.weights is non-uniform, $probability
now reports the weighted mixing proportion W_k / W_total
rather than the observation-count fraction n_k / m.weights is non-uniform, card.min is
now interpreted as a minimum weight sum rather than a minimum
observation count.weights: a cluster with
fewer observations than dimensions could pass the weight-sum-based
card.min check and produce a singular covariance. Cluster
removal now also enforces a minimum observation count.cec(..., keep.removed = TRUE) whenever
a cluster was actually removed during clustering
(Error in matrix(NA, 1, ncol(center)) : non-numeric matrix extent).plot.cec() now warns and skips a single cluster’s
covariance ellipse if it cannot be drawn (e.g. a degenerate covariance),
instead of failing the whole plot. Previously the surrounding
tryCatch had no error handler and so provided
no protection.Initial Release.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.