From: CMA – a comprehensive Bioconductor package for supervised classification with high dimensional data
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] |