anchor.diff:          let xCorr lag v1 v2 = 
               correlationOf Vector.dot lag v1 v2
   
  -
  +    /// Contains correlation functions optimized for matrices
       [<AutoOpen>]
       module Matrix =
       
  
positive.code: namespace FSharp.Stats
  
  module Correlation =
  
      [<AutoOpen>]
      module Seq = 
          /// Pearson correlation 
  
negative.code: 
              kendallCorrFun (FSharp.Stats.Rank.rankFirst setA ) (FSharp.Stats.Rank.rankFirst setB )
  
          /// Biweighted Midcorrelation. This is a median based correlation measure which is more robust against outliers.
          let bicor seq1 seq2 = 
              
              let xs,ys  = seq1 |> Array.ofSeq, seq2 |> Array.ofSeq
  
              let xMed = xs |> Array.median
              let xMad = xs |> Array.medianAbsoluteDev
              let xWeights = xs |> Array.map (bicorHelpers.deviation xMed xMad) |> Array.map bicorHelpers.weight
              let xNF = bicorHelpers.getNormalizationFactor xs xWeights xMed
  
              let yMed = ys |> Array.median
              let yMad = ys |> Array.medianAbsoluteDev
              let yWeights = ys |> Array.map (bicorHelpers.deviation yMed yMad) |> Array.map bicorHelpers.weight
              let yNF = bicorHelpers.getNormalizationFactor ys yWeights yMed
  
              bicorHelpers.sumBy4 (fun xVal xWeight yVal yWeight ->
                  (bicorHelpers.normalize xVal xWeight xNF xMed) * (bicorHelpers.normalize yVal yWeight yNF yMed)
                  )
                  xs xWeights ys yWeights 
  
      /// Contains correlation functions optimized for vectors
      [<AutoOpen>]
      module Vector =
  
  