Linear Discriminant Analysis with Stepwise Feature Selection

Quadratic Discriminant Analysis with Stepwise Feature Selection

Support Vector Machines with Boundrange String Kernel

Support Vector Machines with Exponential String Kernel

Support Vector Machines with Linear Kernel

L2 Regularized Support Vector Machine (dual) with Linear Kernel

Linear Support Vector Machines with Class Weights

L2 Regularized Linear Support Vector Machines with Class Weights

Support Vector Machines with Polynomial Kernel

Support Vector Machines with Radial Basis Function Kernel

This SVM model tunes over the cost parameter and the RBF kernel parameter sigma. In the latter case, using `tuneLength` will, at most, evaluate six values of the kernel parameter. This enables a broad search over the cost parameter and a relatively narrow search over `sigma`

Support Vector Machines with Class Weights

Support Vector Machines with Spectrum String Kernel

Tree Augmented Naive Bayes Classifier Structure Learner Wrapper

Variational Bayesian Multinomial Probit Regression

Adjacent Categories Probability Model for Ordinal Data

Continuation Ratio Model for Ordinal Data

Cumulative Probability Model for Ordinal Data

As of version 3.0.0 of the kohonen package, the argument `user.weights` replaces the old `alpha` parameter. `user.weights` is usually a vector of relative weights such as `c(1, 3)` but is parameterized here as a proportion such as `c(1-.75, .75)` where the .75 is the value of the tuning parameter passed to `train` and indicates that the outcome layer has 3 times the weight as the predictor layer.