Once you have the feature scores from
cosci_is, you can select the features
based on a pre-defined threshold,
using table A.10 in the paper to determine an appropriate threshold or,
using a data driven approach described in the references to select the features and obtain an implicit threshold value.
cosci_is_select implements option 3.
a p vector of scores
what proportion of the p features is noise? If your sample size n is smaller than 100, setting gamma = 0.85 is recommended. Otherwise set gamma = 0.9
Converts the problem of screening out features with lower scores into a problem in large scale multiple testing and uses the procedure described in reference  to determine the signal features.
a vector of selected features
Banerjee, T., Mukherjee, G. and Radchenko P., Feature Screening in Large Scale Cluster Analysis, Journal of Multivariate Analysis, Volume 161, 2017, Pages 191-212
T. Cai, W. Sun, W., Optimal screening and discovery of sparse signals with applications to multistage high throughput studies, J. Roy.Statist. Soc. Ser. B (Statistical Methodology) 79, no. 1 (2017) 197-223
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