The Kriging toolKit (KriKit) is developed at the Institute of Bio- and Geosciences 1 (IBG-1) of Forschungszentrum Jülich (FZJ) under supervision of Lars Freier and Dr. Eric von Lieres.
KriKit features are based on Kriging (also called Gaussian Process Regression) and applicable for data analysis and experimental design. Kriging is an approximation technique where the functional relationships between input and output variables are estimated based on an automatically generated covariance model. It provides not only predictions at arbitrary points but also an estimation of the model prediction error which can be used in further statistical studies, such as optimization. KriKit also contains several tool for data visualization, for instance 3D-plots and movies.
KriKit was implemented and tested using Matlab(2015b). KriKit is freely distributed (under the terms of the GPLv3) as a contribution to the scientific community. If you find it useful for your own work, we would appreciate acknowledgements of the KriKit software.
- Simple way of creating and managing one or several Kriging models
- Kriging prediction can be visualized as
- 2D/3D Interpolation plot
- Screening plots
- Movies
- ...
- Optimization based on expected improvement
- Visualization features can be used via a graphical user interface.
Download the latest release
The toolbox contains a detailed documentation how to use its function via command prompt.