Data Fitting and interp1 - Univariate Function Interpolation

This document introduces numerous functions and algorithms for data fitting and interpolation, including:

- interp1: Univariate function interpolation (performs 1D interpolation using methods like linear, nearest, cubic, etc.)

- spline: Spline interpolation (implements cubic spline interpolation with smooth curve fitting between points)

- polyfit: Polynomial interpolation or fitting (calculates polynomial coefficients for least-squares curve fitting)

- curvefit: Curve fitting (provides nonlinear curve fitting capabilities for custom model functions)

- caspe: Spline interpolation with various boundary conditions (handles different endpoint constraints for spline curves)

- casps: Spline fitting (not available in this implementation)

- interp2: Bivariate function interpolation (performs 2D interpolation on grid-based data)

- griddata: Bivariate interpolation for irregular data (interpolates scattered data points to a regular grid)

- interp: Non-monotonic point interpolation (handles interpolation when data points aren't strictly increasing)

- lagrange: Lagrange interpolation method code (implements the classical polynomial interpolation technique)

Additionally, many other data fitting and interpolation techniques are available, depending on your specific requirements and data characteristics. When selecting an appropriate algorithm, you should consider various factors such as accuracy requirements, computational speed, and memory usage constraints.