Ranks the p features in an n by p design matrix where n represents the sample size and p is the number of features.
cosci_is(dat, min.alpha, small.perturbation = 10^(-6))
n by p data matrix
the smallest threshold (typically set to 0)
a small positive number to remove ties. Default value is 10^(-6)
Uses the univariate merging algorithm
bmt and produces a score
for each feature that reflects its relative importance for clustering.
a p vector of scores
Banerjee, T., Mukherjee, G. and Radchenko P., Feature Screening in Large Scale Cluster Analysis, Journal of Multivariate Analysis, Volume 161, 2017, Pages 191-212
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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