Table 1.
Overview of the classification methods in CMA.
| Method name | CMA function name | Package | Reference |
| Componentwise boosting | compBoostCMA | CMA | [39] |
| Diagonal discriminant analysis | dldaCMA | CMA | [56] |
| Elastic net | ElasticNetCMA | 'glmpath' | [29] |
| Fisher's discriminant analysis | fdaCMA | CMA | [24] |
| Flexible discriminant analysis | flexdaCMA | 'mgcv' | [24] |
| Tree-based boosting | gbmCMA | 'gbm' | [33] |
| k-nearest neighbors | knnCMA | 'class' | [24] |
| Linear discriminant analysis * | ldaCMA | 'MASS' | [56] |
| Lasso | LassoCMA | 'glmpath' | [57] |
| Feed-forward neural networks | nnetCMA | 'nnet' | [24] |
| Probalistic nearest neighbors | pknnCMA | CMA | - |
| Penalized logistic regression | plrCMA | CMA | [58] |
| Partial Least Squares ⋆ + * | pls_ldaCMA | 'plsgenomics' | [5] |
| ⋆ + logistic regression | pls_lrCMA | 'plsgenomics' | [5] |
| ⋆ + random forest | pls_rfCMA | 'plsgenomics' | [5] |
| Probabilistic neural networks | pnnCMA | CMA | [59] |
| Quadratic discriminant analysis | qdaCMA | 'MASS' | [56] |
| Random forest | rfCMA | 'randomForest' | [4] |
| PAM | scdaCMA | CMA | [44] |
| Shrinkage discriminant analysis | shrinkldaCMA | CMA | - |
| Support vector machines | svmCMA | 'e1071' | [60] |
The first column gives the method name, whereas the name of the classifier in the CMA package is given in the second column. For each classifier, CMA uses either own code or code borrowed from another package, as specified in the third column.