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+#' GenSVM: A Generalized Multiclass Support Vector Machine
+#'
+#' The GenSVM classifier is a generalized multiclass support vector machine 
+#' (SVM). This classifier simultaneously aims to find decision boundaries that 
+#' separate the classes with as wide a margin as possible. In GenSVM, the loss 
+#' functions that measures how misclassifications are counted is very flexible.  
+#' This allows the user to tune the classifier to the dataset at hand and 
+#' potentially obtain higher classification accuracy. Moreover, this 
+#' flexibility means that GenSVM has a number of alternative multiclass SVMs as 
+#' special cases. One of the other advantages of GenSVM is that it is trained 
+#' in the primal, allowing the use of warm starts during optimization. This 
+#' means that for common tasks such as cross validation or repeated model 
+#' fitting, GenSVM can be trained very quickly.
+#'
+#' This package provides functions for training the GenSVM model either as a 
+#' separate model or through a cross-validated parameter grid search. In both 
+#' cases the GenSVM C library is used for speed. Auxiliary functions for 
+#' evaluating and using the model are also provided.
+#'
+#' @section GenSVM functions:
+#' The main GenSVM functions are:
+#' \describe{
+#' \item{\code{\link{gensvm}}}{Fit a GenSVM model for specific model 
+#' parameters.}
+#' \item{\code{\link{gensvm.grid}}}{Run a cross-validated grid search for 
+#' GenSVM.}
+#' }
+#'
+#' Other available functions are:
+#' \describe{
+#' \item{\code{\link{plot}}}{Plot the low-dimensional \emph{simplex} space 
+#' where the decision boundaries are fixed.}
+#' \item{\code{\link{predict}}}{Predict the class labels of new data using the 
+#' GenSVM model.}
+#' \item{\code{\link{coef}}}{Get the coefficients of the GenSVM model}
+#' \item{\code{\link{print}}}{Print a short description of the fitted GenSVM 
+#' model}
+#' }
+#'
+#' @author
+#' Gerrit J.J. van den Burg, Patrick J.F. Groenen
+#' Maintainer: Gerrit J.J. van den Burg <gertjanvandenburg@gmail.com>
+#'
+#' @references
+#' Van den Burg, G.J.J. and Groenen, P.J.F. (2016). \emph{GenSVM: A Generalized 
+#' Multiclass Support Vector Machine}, Journal of Machine Learning Research, 
+#' 17(225):1--42. URL \url{http://jmlr.org/papers/v17/14-526.html}.
+#'
+#' @examples
+#'
+#'
+#' @name gensvm-package
+#' @docType package
+#' @import
+NULL
+#>NULL