pyLCD is a Python library for generating samples that minimize the modified Cramér–von Mises distance for standard Gaussian distributions based on the localized cumulative distribution (LCD). These LCD samples can be transformed to match the mean and covariance of non-standard Gaussian distributions.
Currently, only symmetric LCD sample sets for even numbers of samples for standard Gaussian distributions can be generated. These samples can be linearly transformed to match the mean and covariance of non-standard Gaussian distributions.
To use the library, refer to the __main__ section of the lcd_sym_even.py file, which contains an example of how to generate symmetric LCD sample sets for even numbers of samples.
This project is licensed under the MIT License.
Florian Pfaff, pfaff@kit.edu
This work is based on the research and development of methods to minimize the modified Cramér-von Mises distance by Uwe D. Hanebeck, Jannik Steinbring, and Martin Pander.