#' End-member modelling algorithm and supporting functions for unmixing
#' grain-size distributions and further compositional data.
#'
#' EMMAgeo provides a set of functions for end-member modelling analysis
#' (EMMA) of grain-size data and other cases of compositional data. EMMA
#' describes a multivariate data set of m samples, each comprising n parameters
#' (e.g. grain-size classes), as a linear combination of end-member loadings
#' (the underlying distributions) and end-member scores (the contribution of
#' each loading to each sample).\cr EMMA can be run in two principal ways,
#' a deterministic and a robust, including modelling the uncertainties. The
#' deterministic way can be accessed simply with the function \code{EMMA()}.
#' For the robust way there are two protocols that need to be respected. There
#' is a compact protocol, which is mainly automated but needs adjustments by
#' the user, and there is an extended protocol, which allows access to all
#' parameterisation steps of robust EMMA.\cr The package contains further
#' auxiliary functions to check and prepare input data, test parameters and
#' use a graphic user interface for deterministic EMMA. The package also
#' contains an example data set, comprising meaured grain-size distributions
#' of real world sediment end-members.
#'
#' \tabular{ll}{ Package: \tab EMMAgeo\cr Type: \tab Package\cr Version: \tab
#' 0.9.7\cr Date: \tab 2019-05-10\cr License: \tab GPL-3\cr }
#'
#' @name EMMAgeo-package
#' @aliases EMMAgeo
#' @docType package
#' @author Michael Dietze, Elisabeth Dietze
#' @keywords package
#' @import GPArotation
#' @import shiny
#' @importFrom grDevices adjustcolor col2rgb hsv rainbow rgb2hsv
#' @importFrom graphics abline axis barplot contour hist image
#' layout legend lines locator mtext par plot points rug
#' text polygon box segments
#' @importFrom stats median approx complete.cases cor density na.omit
#' quantile rnorm runif sd spline var
#' @importFrom utils setTxtProgressBar txtProgressBar
#' @importFrom limSolve nnls
#' @importFrom caTools runmean
#' @importFrom matrixStats rowMins rowMaxs colVars
NULL
#' example data
#'
#' Robust end-members, a list with output of the function robust.EM()
#'
#' The dataset is the result of the function robust.EM() of the R-package
#' EMMAgeo.
#'
#' @name EMrob
#' @docType data
#' @format The format is: List of 12 $ Vqsn.data :List of 4 ..$ : num [1:15,
#' 1:80] 0.18929 0.184 0.18304 0.00698 0.02033 ...
#' @keywords datasets
#' @examples
#'
#' ## load example data set
#' data(example_EMrob)
#'
NULL
#' example data
#'
#' A list with output of the function test.robustness()
#'
#' The dataset is the result of the function test.robustness() of the R-package
#' EMMAgeo.
#'
#' @name EMpot
#' @docType data
#' @format The format is: List of 8 $ q : num [1:90] 4 4 4 4 4 4 4 4 4 4 ... $
#' lw : num [1:90] 0 0 0 0 0.05 0.05 0.05 0.05 0.1 0.1 ... $ modes : num
#' [1:90] 12 32 61 80 12 32 61 80 12 32 ...
#' @keywords datasets
#' @examples
#'
#' ## load example data set
#' data(example_EMpot)
#'
NULL
#' example data
#'
#' Synthetic data set created by randomly mixed natural end-members
#'
#' The dataset is the result of four mixed natural end-members.
#'
#' @name X
#' @docType data
#' @format num [1:100, 1:116] 0.000899 0.000516 0.00136 0.000989 0.00102 ...
#' @keywords datasets
#' @examples
#'
#' ## load example data set
#' data(example_X)
#'
#' ## extract grain-size classes
#' s <- as.numeric(colnames(X))
#'
#' ## plot first 10 samples stacked in one line plot
#' plot(NA,
#' xlim = c(1, ncol(X)),
#' ylim = c(1, 20))
#'
#' for(i in 1:10) {
#' lines(x = s,
#' y = X[i,] + i)
#' }
#'
#' ## plot grain-size map
#' image(x = s,
#' z = t(X),
#' log = "x",
#' col = rainbow(n = 250))
#'
NULL
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