Creates an interpolation based on arbitrary points.

if your data is already sorted in arrays, consider to use MathNet.Numerics.Interpolation.Barycentric.InterpolateRationalFloaterHormannSorted instead, which is more efficient.

Create an piecewise natural cubic spline interpolation based on arbitrary points, with zero secondary derivatives at the boundaries.

if your data is already sorted in arrays, consider to use MathNet.Numerics.Interpolation.CubicSpline.InterpolateNaturalSorted instead, which is more efficient.

Create a piecewise cubic monotone spline interpolation based on arbitrary points. This is a shape-preserving spline with continuous first derivative.

if your data is already sorted in arrays, consider to use MathNet.Numerics.Interpolation.CubicSpline.InterpolatePchipSorted instead, which is more efficient.

Create a piecewise cubic Akima spline interpolation based on arbitrary points. Akima splines are robust to outliers.

if your data is already sorted in arrays, consider to use MathNet.Numerics.Interpolation.CubicSpline.InterpolateAkimaSorted instead, which is more efficient.

Create a piecewise cubic Hermite spline interpolation based on arbitrary points and their slopes/first derivative.

if your data is already sorted in arrays, consider to use MathNet.Numerics.Interpolation.CubicSpline.InterpolateHermiteSorted instead, which is more efficient.

Create a piecewise linear interpolation based on arbitrary points.

if your data is already sorted in arrays, consider to use MathNet.Numerics.Interpolation.LinearSpline.InterpolateSorted instead, which is more efficient.

Create piecewise log-linear interpolation based on arbitrary points.

if your data is already sorted in arrays, consider to use MathNet.Numerics.Interpolation.LogLinear.InterpolateSorted instead, which is more efficient.

Create a Neville polynomial interpolation based on arbitrary points. If the points happen to be equidistant, consider to use the much more robust PolynomialEquidistant instead. Otherwise, consider whether RationalWithoutPoles would not be a more robust alternative.

if your data is already sorted in arrays, consider to use MathNet.Numerics.Interpolation.NevillePolynomialInterpolation.InterpolateSorted instead, which is more efficient.

Create a barycentric polynomial interpolation where the given sample points are equidistant.

if your data is already sorted in arrays, consider to use MathNet.Numerics.Interpolation.Barycentric.InterpolatePolynomialEquidistantSorted instead, which is more efficient.

Create a Floater-Hormann rational pole-free interpolation based on arbitrary points.

if your data is already sorted in arrays, consider to use MathNet.Numerics.Interpolation.Barycentric.InterpolateRationalFloaterHormannSorted instead, which is more efficient.

Create a Bulirsch Stoer rational interpolation based on arbitrary points.

if your data is already sorted in arrays, consider to use MathNet.Numerics.Interpolation.BulirschStoerRationalInterpolation.InterpolateSorted instead, which is more efficient.

Create a step-interpolation based on arbitrary points.

if your data is already sorted in arrays, consider to use MathNet.Numerics.Interpolation.StepInterpolation.InterpolateSorted instead, which is more efficient.