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Hi. It depends on what you're doing. If you're only interested in reducing the number of features for purposes of constructing a predictive model using fewer features, then it is usually not necessary to do any multiple testing adjustments. The repository's main page lists a few papers, all of which use feature screening successfully in slightly different ways (no adjustments). The reason is that for prediction it does not matter so much if some irrelevant features happen to survive the screening.
In case you are interested only detecting which features are truly relevant, and false discoveries matter, then you of course need to make adjustments, and there's a large literature on that. But that is not really the purpose of this package
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Closing this issue now.
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