cluster.bic() passes its eps argument on
to count.parameters(), it always used the default threshold
beforehmm.clust() passes its smoothing argument
on to assign.clusters(), cluster assignment always smoothed
with the default beforesmooth.probabilities() returns a uniform distribution
for a set of probabilities that are all zero, which used to stay zero
and is then rejected by build_hmm()smooth.probabilities() no longer returns negative
probabilities. Absolute discounting assumes the smoothing parameter is
smaller than the smallest non-zero probability, and the amount
discounted is now lowered to respect that. A probability below the
parameter used to be discounted to a negative value, which survived
because the row still sums to one, and grew more negative every time the
model was smoothed again. Estimates for data with sparse emissions
change, see belowselect.seeds() stops with a message when too few
sequences are left to fill a seed, it used to select NA
idsseq_along(), they could run an iteration on an empty
setassign.clusters() and select.seeds() instead
of one at a time, which makes the algorithm about five times faster on
1000 sequences and leaves all results unchangedhmm.clust() has an argument max.iter and
size.search() has an argument max.states to
bound the search for cluster memberships and hidden states\donttest
instead of \dontrun, they are slow rather than impossible
to runsmooth.probabilities() above. On the
simulation study of the accompanying paper nothing changes but the BIC,
by 0.0001 percent. On the full Swiss Household data the cluster models
and a small number of cluster memberships change, the number of clusters
and their sizes do notAuthor and Maintainer with
Authors@RLazyData, the package has no data
directory.data pronoun,
which silences the deprecation warnings from ggplot2
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