R/RGLUEANN.R

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

#' Non-linear probabilistic data-driven modelling with RGLUEANN
#'
#' 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. \url{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, \url{http://rogiersbart.blogspot.com/p/publications.html}.
#'
#' @import AMORE Hmisc
#' @docType package
#' @name RGLUEANN
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