TableCurve 2D v5.01
Introduction
TableCurve 2D's built-in library includes a wide array of linear and nonlinear models for any application including equations that may never have been considered. - from simple linear equations to high order Chebyshev polynomials.
TableCurve 2D is the automatic choice for curve-fitting and data modeling for critical research. TableCurve 2D’s state-of-the-art data fitting includes capabilities not found in other software packages:
- A 38-digit precision math emulator for properly fitting high order polynomials and rationals.
- A robust fitting capability for nonlinear fitting that effectively copes with outliers and a wide dynamic Y data range.
- An AI Expert option that automatically selects the appropriate peak, transition or kinetics models for you.
Automation Takes The Trial and Error Out of Curve Fitting
Fit all of TableCurve 2D’s 3,665 built-in equations or just the ones you need — instantly! With TableCurve 2D, a single mouse click is all it takes to start the automated curve fitting process there is no set up required. TableCurve saves you precious time because it takes the endless trial and error out of curve fitting.
Fit User Defined Equations
Up to 15 user-defined equations can be entered and ranked along with the built-in equations. These specialised models can contain most mathematical constructs, including special functions, series convergence and conditional statements, differentiations, integrations, and parameter constraints. And, unlike most curve fitting programs, TableCurve 2D's user-defined functions are compiled so custom curve fitting can be performed quickly -- at nearly the speed as with the built-in equations. You can also add up to 100 external C or FORTRAN language functions to the TableCurve 2D equation set. These equations and constraints can be of unlimited complexity.
Accurately Extrapolate Any Data Set
Increase the accuracy of your predictions with state-of-the-art AR (Autoregressive) procedures that offer the means to effectively extrapolate any data set. Select from any one of the 9 different procedures for extrapolating your data - 3 to predict ahead, 3 to predict earlier data, and 3 that predict in both directions. Of these algorithms, six offer in-situ noise removal using advanced SVD and Eigendecomposition methods.