Model
Interpolation
Create surfaces from sample data using these interpolation methods:

Inverse distance weighted

Radialbased functions, which include the following kernels

Thin plate spline

Spline with tension

Multiquadratic

Inverse multiquadratic

Completely regularized spline kernels

Global and local polynomials

Kriging for exact data and for errorcontaminated data

Ordinary, for data with unknown constant mean value

Simple, for data with known mean value

Universal, for data with mean value as a function on coordinates

Indicator, for discrete data or data transformed to discrete

Probability, for discrete data as primary variable and continuous data as secondary variables

Disjunctive, for nonlinear predictions

Cokriging (multivariate version of the abovementioned kriging models)

Isotropical or anisotropical models
Kriging Output Surface Types

Prediction

Prediction standard error (measure of the prediction quality)

Probability map (probability that specified threshold value is exceeded)

Error of indicators (measure of the probability map uncertainty)

Quantile map (over and underpredicted values)
Modeling Tools for Kriging

Data transformations

Box–Cox

Logarithmic

Arcsine

Normal score

Data detrending

Global polynomial

Local polynomial

Variography

Models (four can be used simultaneously)

Nugget

Circular

Spherical

Tetraspherical

Pentaspherical

Exponential

Gaussian

Rational quadratic

Hole effect

KBessel

JBessel

Stable

Semivariogram/Covariance surface

Anisotropy

Specifying or estimating the proportion of measurement error in the nugget

Crosscovariance option for shift between variables

Estimation of all or part of the model parameters by a modified weighted least squares algorithm

Declustering

Checking for data bivariate distribution
Searching Neighbourhood
To select neighbouring data to predict the value for the target point

Ellipse with one, four, or eight angular sectors

Minimum and maximum number of points in each sector