Class  Description 

AkimaSpline 
DataProcessor that interpolates incoming data using an Akima spline
interpolation scheme.

AkimaSplineSmoother 
Optimization algorithm which attempts to smooth data without moving any
point far from its original location.

AkimaSplineSmootherApp 
App to drive AkimaSplineSmoother

AkimaSplineSmootherApp.Applet  
AkimaSplineSmootherApp.IntegratorSmoother  
AkimaSplineSmootherDy 
Optimization algorithm which attempts to smooth data without moving any
point far from its original location.

AkimaSplineSmootherDyApp 
App to drive AkimaSplineSmoother

AkimaSplineSmootherDyApp.Applet  
AkimaSplineSmootherDyApp.IntegratorSmoother  
AkimaSplineSmootherMain 
Main method to drive AkimaSplineSmoother

ArrayReader1D 
Reads in lots of 1D arrays from a file.

B9Optimizer 
Class to determine B9 value that best fit the simulation results

B9Optimizer.VirialParam  
CalcGradientDifferentiable 
Uses finite difference methods to determine the second order differential of the potential (i.e.

FiniteDifferenceDerivative  
FittingFunctionNonLinear 
This class is coded for specific nonlinear model that decribes the Equation:
f(x) = polynomialA + exp(Kx^L)*polynomialB
M and N are the input parameters that determine the order of polynomialA
and polynomialB respectively
polynomialA is given as:
M
polynomialA = sum [ a_m * x^m ]
m=1
for example, when M=3; polynomialA = a_1 * x + a_2 * x^2 + a_3 * x^3
Note: polynomialB has same form as polynomialA

FittingFunctionNonLinearB 
This class is coded for specific nonlinear model that decribes the Equation:
f(x) = [ 1 + exp(Kx^L) ] * polynomialA
M is the input parameter that determine the order of polynomialA
polynomialA is given as:
M
polynomialA = sum [ a_m * x^m ]
m=1
for example, when M=3; polynomialA = a_1 * x + a_2 * x^2 + a_3 * x^3

Function1d  
LinearFit 
Class that performs a linear fit to x,y data, optionally taking weights
associated with each data point.

NewtonMinimization  
NonLinearCurveDiffFitting 
Similar as

NonLinearCurveFitting 
Class that compute
Reference: NUMERICAL RECIPES in Fortran 2nd Ed.

PadeApproximation 
Class to approximate a power series with rational function (Pade Approximation)
Pade[K/L] = A_K/ C_L = B_M
with Kth, Lth and Mth order, where K + L = M
a0 + a1*x + a2*x^2 + ...

PadeApproximation.VirialParam  
PolynomialFit 
Class that performs a polynomial fit to x,y data, optionally taking weights
associated with each data point.

SteepestDescent  
VirialOptimizer 
Class to determine B9 value that best fit the simulation results after
Pade Approximation [K/L]

VirialOptimizer.VirialParam 