Depends: R (>= 3.0), mvmesh, geometry, SphericalCubature (>= 1.5), rgl,

Title: Resampling Based Yield Stability Analyses
Description: Several yield stability analyses are mentioned in this package: variation and regression based yield stability analyses. Resampling techniques are integrated with these stability analyses. The function stab.mean() provides the genotypic means and ranks including their corresponding confidence intervals. The function stab.var() provides the genotypic variances over environments including their corresponding confidence intervals. The function stab.fw() is an extended method from the Finlay-Wilkinson method (1963). This method can include several other factors that might impact yield stability. Resampling technique is integrated into this method. A few missing data points or unbalanced data are allowed too. The function stab.fw.check() is an extended method from the Finlay-Wilkinson method (1963). The yield stability is evaluated via common check line(s). Resampling technique is integrated.

Title: A Generalized Multiclass Support Vector Machine
Authors@R: c(person("Gertjan", "van den Burg", role=c("aut", "cre"),
Description: The GenSVM classifier is a generalized multiclass support vector
machine (SVM). This classifier aims to find decision boundaries that
separate the classes with as wide a margin as possible. In GenSVM, the
loss function is very flexible in the way that misclassifications are
penalized.  This allows the user to tune the classifier to the dataset
at hand and potentially obtain higher classification accuracy than
alternative multiclass SVMs.  Moreover, this flexibility means that
GenSVM has a number of other multiclass SVMs as special cases. One of
the other advantages of GenSVM is that it is trained in the primal
space, 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. Based on: G.J.J. van den
Burg and P.J.F. Groenen (2018) <http://www.jmlr.org/papers/v17/14-526.html>.
Author: Gertjan van den Burg [aut, cre],