RGLUEANN: Non-linear probabilistic data-driven modelling with RGLUEANN

In rogiersbart/RGLUEANN: Non-linear probabilistic data-driven modelling using general likelihood uncertainty estimation and artificial neural networks

Description

The RGLUEANN package provides basic functionality for training a GLUE-ANN model ensemble, performing cross-validation and making predictions.

References

Rogiers B, Mallants D, Batelaan O, Gedeon M, Huysmans M and Dassargues A (2012). Estimation of hydraulic conductivity and its uncertainty from grain-size data using GLUE and artificial neural networks. Mathematical Geosciences, 44(6), pp. 739-763. http://dx.doi.org/10.1007/s11004-012-9409-2.

Rogiers B (2013). Multi-scale aquifer characterization: from outcrop analogue, direct-push and borehole investigations towards improved groundwater flow models. PhD thesis, Faculty of Science, KU Leuven. ISBN 978-90-8649-672-3 - D/2013/10.705/87 - ISSN 0250-7803, http://rogiersbart.blogspot.com/p/publications.html.

rogiersbart/RGLUEANN documentation built on May 27, 2019, 12:16 p.m.