Kriging is an invaluable tool in understanding the behavior of sparse data. It has proven effective in many aspects of engineering and in applications where data is "expensive", or difficult, to collect.
For information on how the math behind Kriging works, please refer to these excellent sources:
A Taxonomy of Global Optimization Methods Based on Response Surfaces by Donald R. Jones.
Engineering Design via Surrogate Modelling by Forrester, Sóbester and Keane.
The goal of this toolbox is to make Kriging easily accessible in Python. The module is constant development, so please check back occasionally to see progress. If you encounter any problems, please report them on the Github Issue tracker.