PEAXAI_counterfactuals
                        Projection-Based Efficiency counterfactuals
PEAXAI_fitting          Training Classification Models to Estimate
                        Efficiency
PEAXAI_global_importance
                        Global feature importance for efficiency
                        classifiers
PEAXAI_local_importance
                        Local feature importance for efficiency
                        classifiers
PEAXAI_peer             Identify Benchmark Peers Based on Estimated
                        Efficiency Probabilities
PEAXAI_predict          Predict Probability of Efficiency Using a
                        Fitted Model
PEAXAI_ranking          Generate Efficiency Rankings Based on
                        Probabilistic Classification
SMOTE_Z_data            Create New SMOTE Units to Balance Data
                        combinations of m + s
SMOTE_data              Create New SMOTE Units to Balance Data
                        combinations of m + s
convex_facets           Identify Maximal Facets of the Convex Frontier
data                    Simulated efficiency dataset (100 DMUs)
data_SABI               Spanish Food Industry Firms Dataset
data_example            Simulated efficiency dataset (100 DMUs)
find_beta_maxmin        Search Range for Directional Efficiency
                        Parameter (beta)
firms                   Spanish Food Industry Firms Dataset
get_SMOTE_DMUs          Create New SMOTE Units to Balance Data
                        combinations of m + s
label_efficiency        Data preprocessing and efficiency labeling with
                        Additive DEA
preprocessing           Prepare Data and Handle Errors
train_PEAXAI            Training a Classification Machine Learning
                        Model
xai_prepare_sets        Prepare Training and Target Datasets from a
                        caret Model
