anchor.diff:                                  gradientVector)
               }
   
  -    /// Logistic function of the form "y = L/(1+e^(k(t-x)))"
  +        /// Logistic function of the form "y = L/(1+e^(k(t-x)))"
           let LogisticFunctionDescending = {
               ParameterNames= [|"L - curve maximum";"k - Steepness"; "x0 xValue of midpoint"|]
               GetFunctionValue = (fun (parameterVector:Vector<float>) xValue -> parameterVector.[0] / (1. + exp(parameterVector.[1]*(xValue-parameterVector.[2]))))
  
positive.code:             InitialParamGuess       = initialParamGuess
              }
  
      /////////////////////////
      /// Hill equation "y = Vm * x^n / (k^n+x^n)"
          let hillModel = 
              let parameterNames = [|"Vm";"n";"k"|]
              let getFunctionValues = (fun (parameters:Vector<float>) x -> 
  
negative.code:             }
  
      
          /////////////////////////
          /// Exponentially modified Gaussian (EMG) of the form "y =  ((amp*std)/tau) * sqrt(PI/2.) * exp(1./2. * ((std/tau)**2.) - ((x-meanX)/tau)) * Erfc((1./sqrt(2.)) * ((std/tau)-((x-meanX)/std)))"
  
  
          let private findZ initMeanX initStdev initTau x =
  