| Methods | Name | R package | Description | Key reference |
| ANN | Artificial Neural Networks | nnet | A machine learning method with the mean of three runs used to provide predictions and projections, as each simulation gives slightly different results | Ripley (1996) |
| CTA | Classification Tree Analysis | rpart | A classification method running a 50-fold cross-validation to select the best trade-off between the number of leaves of the tree and the explained deviation | Breiman et al. (1984) |
| GAM | Generalized Additive Models | gam | A regression method with 4 degrees of freedom and a stepwise procedure to select the most parsimonious model | Hastie and Tibshirani (1990) |
| GBM | Generalized Boosting Model | gbm | A machine learning method that combines a boosting algorithm and a regression tree algorithm to construct an “ensemble” of trees | Ridgeway (1999); Friedman (2001) |
| GLM | Generalized Linear Models | stats | A regression method with polynomial terms for which a stepwise procedure is used to select the most significant variables | McCullagh and Nelder (1989) |
| MARS | Multiple Adaptive Regression Splines | mda | A major assumption of any linear process in that the coefficients are stable across all levels of the explanatory variables | Friedman (1991) |
| FDA | Flexible Discriminant Analysis | mda | A classification method based on mixture models | Hastie et al. (1994) |
| RF | Breiman and Cutler’s Random Forest for Classification and Regression | Random Forest | A machine learning method that is a combination of tree predictors such that each tree depends on the values of a random vector sample independently and has the same distribution for all trees in the forest | Breiman (2001) |