OPTION #1 (the three blocks of the G-SS-TDA process are in separate function): | First step of the process: dsga. | Second step of the process: Select the genes within the dsga object created in the previous step and calcute the values of the filtering functions. | Third step of the process: Create the mapper object with disease component matrix | with only the selected genes and the filter function obtained in the gene selection step. | OPTION #2 (all process integrate in the same function):

A brief introduction to production theory field | Summary of eat functions | The PISAindex database | Modeling a scenario with an input and an output. Plotting the frontier | EAT() | Categorical variables | frontier() | Modeling a multioutput scenario. Feature selection. | rankingEAT() | Graphical representation by a tree structure | plotEAT() | The EAT hyperparameter tuning | bestEAT() | Efficiency scores. Graphical representation. | Efficiency Analysis Trees model: efficiencyEAT() | Convexified Efficiency Analysis Trees model: efficiencyCEAT() | efficiencyJitter() | efficiencyDensity() | Random Forest | RFEAT() | plotRFEAT() | rankingRFEAT() | bestRFEAT() | efficiencyRFEAT() | Predictions | predict()