Description Usage Arguments Details Value References See Also Examples

Solves an L1 relaxed univariate clustering criterion and returns a
sequence of *λ* values where the clusters merge

1 | ```
bmt(x, alpha = 0.1, small.perturbation = 10^(-6))
``` |

`x` |
observation vector |

`alpha` |
merging threshold. Default is 0.1 |

`small.perturbation` |
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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