The issue is more complex than I thought. While for monotonic pvalues the strategy works, but if many identical pvalues exist, the sorting corrupts the q value smoothing. If many identical keys exist (pvalues), it is not clear which index to choose.

Reproduce

#r "nuget: Plotly.NET, 2.0.0-preview.16"
open Plotly.NET

let index = Array.init 10000 id
let testValues =
	[|
		[|1. .. 5000.|]
		Array.init 2000 (fun x-> 5000.)
		[|5001..8000|]
	|]
	|> Array.concat

testValues |> Array.indexed |> Chart.Point |> Chart.show
System.Array.Sort(testValues,index)
index |> Array.indexed |> Chart.Point |> Chart.show

Edit: When Seq.sort or List.sort is used instead of Array.Sort the problem seems to be solved.

The standard q value implementation is fixed. I decided to omit the bindBy function, since it reduces the readability and causes harm when the p value collection is too large. The monotonization of the q values is now packed within the respective function. Unit tests must be corrected and the Qvalues.ofPvaluesRobust requires further inspection of validity and proper documentation.

The robust q value version has an additional term, that corrects small p values, especially when the number of tests is low. Its described in Storey, J.D. (2002), A direct approach to false discovery rates. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 64: 479-498. https://doi.org/10.1111/1467-9868.00346 in function 9.