Simulates p-value for dip test (see `dip`

)
in the way suggested by Tantrum, Murua and Stuetzle (2003) from the
clostest unimodal distribution determined by kernel density estimation
with bandwith chosen so that the density just becomes unimodal. This is
less conservative (and in fact sometimes anti-conservative) than the
values from `dip.test`

.

`dipp.tantrum(xdata,d,M=100)`

xdata

numeric vector. One-dimensional dataset.

d

numeric. Value of dip statistic.

M

integer. Number of artificial datasets generated in order to estimate the p-value.

List with components

approximated p-value.

borderline unimodality bandwith in `density`

with default settings.

vector of dip statistic values from simulated artificial data.

J. A. Hartigan and P. M. Hartigan (1985) The Dip Test of
Unimodality, *Annals of Statistics*, 13, 70-84.

Tantrum, J., Murua, A. and Stuetzle, W. (2003) Assessment and
Pruning of Hierarchical Model Based Clustering, *Proceedings of the
ninth ACM SIGKDD international conference on Knowledge discovery and
data mining*, Washington, D.C., 197-205.

# NOT RUN { # not run, requires package diptest # x <- runif(100) # d <- dip(x) # dt <- dipp.tantrum(x,d,M=10) # }