object EmpiricalVariogram extends Serializable

This creates an empirical variogram from the dataset, which is then used to fit into one of the semivariogram ModelType for use in Kriging Interpolation

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  1. def apply(length: Int): EmpiricalVariogram
  2. def linear(pts: Array[PointFeature[Double]], radius: Option[Double] = None, lag: Double = 0.0): Array[(Double, Double)]

    Computes empirical semivariogram for Linear model

  3. def nonlinear(pts: Array[PointFeature[Double]], maxdist: Double, binmax: Int): EmpiricalVariogram

    Computes empirical semivariogram for Spherical, Gaussian, Exponential, Circular and Wave models

    Computes empirical semivariogram for Spherical, Gaussian, Exponential, Circular and Wave models

    pts

    PointFeature array for creating the variogram

    maxdist

    The bandwidth of variations that the empirical variogram is supposed to capture

    binmax

    The maximum number of bins in the empirical variogram to be created