Solves an L1 relaxed univariate clustering criterion and returns a sequence of λ values where the clusters merge
bmt(x, alpha = 0.1, small.perturbation = 10^(-6))
merging threshold. Default is 0.1
a small positive number to remove ties. Default is 10^(-6)
solves a convex relaxation of the univariate clustering criterion given by equation (2) in the referenced paper and generates a sequence of cluster merges and corresponding λ values. See algorithm 1 in the referenced paper for more details.
path - number of clusters on the big merge path
lambda.path - sequence of lambda where clusters merge
index - cluster index at the point where clusters merge
merge - merge points
split - split points
prob - merging proportion
boundaries - cluster boundaries
P. Radchenko, G. Mukherjee, Convex clustering via l1 fusion penalization, J. Roy. Statist, Soc. Ser. B (Statistical Methodology) (2017) doi:10.1111/rssb.12226.
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