gpR: Gaussian processes in R

# gpR: Gaussian processes in R
#
# Copyright (C) 2015 - 2016 Simon Dirmeier
#
# This file is part of gpR.
#
# gpR is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# gpR is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with gpR. If not, see <http://www.gnu.org/licenses/>.

#' gpR
#'
#' \emph{gpR} is a Bayesian machine learning package using latent Gaussian processes.
#' In general supervised machine learning can be divided in classification,
#' where we describe data using discrete labels, and regression, where the labels are continuous.
#'
#' @useDynLib gpR
#'
#' @name gpR-package
#' @author Simon Dirmeier | \email{simon.dirmeier@@gmx.de}
#' @docType package
#' @keywords package
#'
#' @references
#'  Rasmussen C.E. and Williams C.K.I. (2006), \emph{Gaussian Processes for Machine Learning}, MIT Press \cr
#'  \url{http://www.gaussianprocess.org/gpml/} \cr \cr
#'  Barber D. (2015), \emph{Bayesian Reasoning and Machine Learning}, Cambridge University Press \cr
#'  \url{http://web4.cs.ucl.ac.uk/staff/D.Barber/pmwiki/pmwiki.php?n=Brml.Online}
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