INTERP.1D.NEVILLE Function

INTERP.1D.NEVILLE

Lagrange Polynomial Interpolation using Neville's Algorithm. Supports both differentiation and integration.

Syntax

=INTERP.1D.NEVILLE(dataX, dataY, queryPoints, extrapolate)

Parameters

Name	Type	Required	Description
dataX	Double[]	Yes	Known x values.
dataY	Double[]	Yes	Known y values.
queryPoints	Object	Yes	Points to interpolate at.
extrapolate	Boolean	No	Allow extrapolation.

Returns

Calculated result based on input parameters

Examples

Error Conditions

Error	Cause	Solution

About NEVILLE Interpolation

Interpolation estimates unknown values by fitting a function through known data points. The NEVILLE method provides a specific approach to constructing this interpolating function.

Key Characteristics

When to Use:

• When you have discrete data points and need intermediate values
• For smoothing or curve fitting applications
• When analytical functions are not available

Practical Applications

• Data Analysis: Fill missing data points in time series
• Scientific Computing: Resample data to different grids
• Engineering: Smooth sensor readings
• Graphics: Generate smooth curves from control points

Related Functions

INTERP.1D.LINEAR

Linear Spline Interpolation.

INTERP.1D.CUBIC.SPLINE

Cubic Spline Interpolation.

INTERP.1D.AKIMA

Akima Spline Interpolation. A special case of cubic spline that is more robust to outliers.

INTERP.1D.PCHIP

Monotone Cubic Spline (Pchip). A special case of cubic spline that preserves monotonicity of the data.

INTERP.1D.STEP

Step Interpolation. A step function where the interpolated value is always the value of the first key which is nearest to and less than or equal to the evaluation point.

INTERP.1D.LOGLINEAR

Piece-wise Log-Linear Interpolation. This is equivalent to a linear interpolation in the log-scale.

See Also

• Method Comparison - Compare different methods
• Quick Start Guide - Get started with the add-in