Data Fitting Techniques

This document presents various methods and functions for data fitting:

- % Data Fitting

- % interp1 - 1D interpolation: Linear, nearest-neighbor, cubic spline and other interpolation methods for single-variable functions

- % spline - Spline interpolation: Implements cubic spline interpolation with natural boundary conditions

- % polyfit - Polynomial interpolation or fitting: Computes polynomial coefficients that best fit the data using least squares method

- % curvefit - Curve fitting: General function for fitting curves to data points with customizable models

- % caspe - Spline interpolation with various boundary conditions: Supports different endpoint constraints for spline interpolation

- % casps - Spline fitting (Not available): Function for spline-based fitting (currently unimplemented)

- % interp2 - 2D interpolation: Bilinear and bicubic interpolation methods for two-dimensional data grids

- % griddata - 2D interpolation for irregular data: Interpolates scattered data to regular grids using various methods

- % *interp - Non-monotonic node interpolation: Handles interpolation when data points are not monotonically ordered

- % *lagrange - Lagrange interpolation method: Implements classical polynomial interpolation using Lagrange basis polynomials

Data fitting refers to mathematical techniques that find one or more function curves that approximate given data points. Methods such as 1D function interpolation, spline interpolation, polynomial interpolation/fitting, and curve fitting can all be employed for data fitting. Additionally, 2D interpolation for irregular data and non-monotonic node interpolation help process various types of data more effectively. For implementing Lagrange interpolation, the lagrange function can be utilized. It's important to note that the spline fitting method casps is not mentioned as available in this context. The asterisk (*) denotes methods that may require custom implementation or specialized toolboxes.