balanced.cv.fold | Split a dataset for Cross Validation taking into account class balance |

balanced.loss.weights | Compute loss.weights so that total losses of each class is balanced |

bhattacharyya.coefficient | Compute Bhattacharyya coefficient needed for Hellinger distance |

binaryClassificationLoss | Loss functions for binary classification |

costMatrix | Compute or check the structure of a cost matrix |

epsilonInsensitiveRegressionLoss | Loss functions to perform a regression |

fbetaLoss | Loss functions for binary classification |

gradient | Return or set gradient attribute |

gradient.default | Return or set gradient attribute |

gradient<- | Return or set gradient attribute |

gradient<-.default | Return or set gradient attribute |

hclust_fca | Find first common ancestor of 2 nodes in an hclust object |

hellinger.dist | Compute Hellinger distance |

hingeLoss | Loss functions for binary classification |

is.convex | Return or set is.convex attribute |

is.convex.default | Return or set is.convex attribute |

is.convex<- | Return or set is.convex attribute |

is.convex<-.default | Return or set is.convex attribute |

iterative.hclust | Perform multiple hierachical clustering on random subsets of a dataset |

ladRegressionLoss | Loss functions to perform a regression |

linearRegressionLoss | Loss functions to perform a regression |

lmsRegressionLoss | Loss functions to perform a regression |

logisticLoss | Loss functions for binary classification |

lpSVM | Linearly Programmed SVM |

lvalue | Return or set lvalue attribute |

lvalue.default | Return or set lvalue attribute |

lvalue<- | Return or set lvalue attribute |

lvalue<-.default | Return or set lvalue attribute |

mmc | Convenient wrapper function to solve max-margin clustering problem on a dataset |

mmcLoss | Loss function for max-margin clustering |

multivariateHingeLoss | The loss function for multivariate hinge loss |

nrbm | Convex and non-convex risk minimization with L2 regularization and limited memory |

nrbmL1 | Convex and non-convex risk minimization with L2 regularization and limited memory |

ontologyLoss | Ontology Loss Function |

ordinalRegressionLoss | The loss function for ordinal regression |

predict.mmc | Predict class of new instances according to a mmc model |

predict.svmLP | Linearly Programmed SVM |

predict.svmMLP | Linearly Programmed SVM |

preferenceLoss | The loss function for Preference loss |

print.roc.stat | Generic method overlad to print object of class roc.stat |

quantileRegressionLoss | Loss functions to perform a regression |

rank.linear.weights | Rank linear weight of a linear model |

roc.stat | Compute statistics for ROC curve plotting |

rocLoss | Loss functions for binary classification |

rowmean | Columun means of a matrix based on a grouping variable |

softMarginVectorLoss | Soft Margin Vector Loss function for multiclass SVM |

softmaxLoss | softmax Loss Function |

svmLP | Linearly Programmed SVM |

svmMulticlassLP | Linearly Programmed SVM |

wolfe.linesearch | Wolfe Line Search |