anchor.diff:      //defines a function that gives the unconstrained spline value for a given x value
       //the AICc is reported as model selection criterion
       let unconstrainedSpline = 
  -        Hermite.getInitialEstimateOfWXY weighting means x_Values
  +        TemporalClassification.getInitialEstimateOfWXY weighting means x_Values
           |> fun result -> result.AICc,result.SplineFunction
   
       //defines a function that gives the constrained spline value for a given x value
       //Constraint: One minimum
       //the AICc is reported as model selection criterion
       let monotoneSpline = 
  -        Hermite.splineIncreasing x_Values means weighting 0
  +        TemporalClassification.splineIncreasing x_Values means weighting 0
           |> fun (constraintMatrices,result) -> result.AICc,result.SplineFunction
   
       //defines a function that gives the constrained spline value for a given x value
       //Constraint: A maximum, then a minimum
       //the AICc is reported as model selection criterion
       let minMaxSpline = 
  -        Hermite.splineIncreasing x_Values means weighting 2  
  +        TemporalClassification.splineIncreasing x_Values means weighting 2  
           |> fun (constraintMatrices,result) -> result.AICc,result.SplineFunction
   
       //defines the x_Values, the functions should be fitted with
  
positive.code: let rawDataChart =   
      //calculates the means and standard deviations of the three replicates and plots it as line
      let line =
          Hermite.calcMeanOfRep y_Values 3
          |> Seq.indexed
          |> Chart.Line
          |> Chart.withTraceName (sprintf "means")
          |> Chart.withYErrorStyle(Array=Hermite.calcStDevOfRep y_Values 3)
  
      //plots all measurements as points 
      let points =
  
negative.code: let splineChart =
  
      let means = 
          TemporalClassification.calcMeanOfRep y_Values 3
  
      //plot the means of the replicates as gray dots
      let origPoints = 
  