R: Probabilistic Efficiency Analysis Using Explainable Artificial Intelligence

# Probabilistic Efficiency Analysis Using Explainable Artificial Intelligence

## Documentation for package ‘PEAXAI’ version 1.0.2

- DESCRIPTION file
- User guides, package vignettes and other documentation
- Package README

## Help Pages

convex_facets | Identify Maximal Facets of the Convex Frontier
data | Simulated efficiency dataset (100 DMUs)
data_example | Simulated efficiency dataset (100 DMUs)
data_SABI | Spanish Food Industry Firms Dataset
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
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
preprocessing | Prepare Data and Handle Errors
SMOTE_data | Create New SMOTE Units to Balance Data combinations of m + s
SMOTE_Z_data | Create New SMOTE Units to Balance Data combinations of m + s
train_PEAXAI | Training a Classification Machine Learning Model
xai_prepare_sets | Prepare Training and Target Datasets from a caret Model