Package: PEAXAI 1.0.2

PEAXAI: Probabilistic Efficiency Analysis Using Explainable Artificial Intelligence

Provides a probabilistic framework that integrates Data Envelopment Analysis (DEA) (Banker et al., 1984) <doi:10.1287/mnsc.30.9.1078> with machine learning classifiers (Kuhn, 2008) <doi:10.18637/jss.v028.i05> to estimate both the (in)efficiency status and the probability of efficiency for decision-making units. The approach trains predictive models on DEA-derived efficiency labels (Charnes et al., 1985) <doi:10.1016/0304-4076(85)90133-2>, enabling explainable artificial intelligence (XAI) workflows with global and local interpretability tools, including permutation importance (Molnar et al., 2018) <doi:10.21105/joss.00786>, Shapley value explanations (Strumbelj & Kononenko, 2014) <doi:10.1007/s10115-013-0679-x>, and sensitivity analysis (Cortez, 2011) <https://CRAN.R-project.org/package=rminer>. The framework also supports probability-threshold peer selection and counterfactual improvement recommendations for benchmarking and policy evaluation. The probabilistic efficiency framework is detailed in González-Moyano et al. (2025) "Probability-based Technical Efficiency Analysis through Machine Learning", in review for publication.

Authors:Ricardo González Moyano [cre, aut], Juan Aparicio [aut], José Luis Zofío [aut], Víctor España [aut]

PEAXAI_1.0.2.tar.gz
PEAXAI_1.0.2.zip(r-4.7)PEAXAI_1.0.2.zip(r-4.6)PEAXAI_1.0.2.zip(r-4.5)
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PEAXAI_1.0.2.tar.gz(r-4.7-any)PEAXAI_1.0.2.tar.gz(r-4.6-any)
PEAXAI_1.0.2.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
PEAXAI/json (API)

# Install 'PEAXAI' in R:

install.packages('PEAXAI', repos = c('https://rgonzalezmoyano.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/rgonzalezmoyano/peaxai/issues

Datasets:

data - Simulated efficiency dataset
data_example - Simulated efficiency dataset
data_SABI - Spanish Food Industry Firms Dataset
firms - Spanish Food Industry Firms Dataset

On CRAN:

4.00 score 528 downloads 11 exports 179 dependencies

Last updated from:1d0c714f1e. Checks:7 WARNING, 1 ERROR, 1 OK. Indexed: yes.

The latest version of this package failed to build. Look at thebuild logs for more information.

Target	Result	Time
linux-devel-x86_64	WARNING	296
source / vignettes	ERROR	361
linux-release-x86_64	WARNING	284
macos-release-arm64	WARNING	163
macos-oldrel-arm64	WARNING	152
windows-devel	WARNING	188
windows-release	WARNING	174
windows-oldrel	WARNING	197
wasm-release	OK	222

Exports:find_beta_maxmin label_efficiency PEAXAI_counterfactuals PEAXAI_fitting PEAXAI_global_importance PEAXAI_local_importance PEAXAI_peer PEAXAI_predict PEAXAI_ranking SMOTE_data SMOTE_Z_data

Dependencies:adabag alabama askpass assertthat backports base64enc Benchmarking boot bslib cachem caret cccp checkmate class cli clock cluster codetools coin colorspace ConsRank cpp11 crosstalk crs cubature Cubist curl data.table deaR diagram digest doFuture doParallel dplyr e1071 evaluate farver fastmap fitdistrplus fontawesome foreach foreign Formula fs future future.apply generics ggplot2 glmnet globals glue gower gridExtra gtable gtools hardhat highr Hmisc htmlTable htmltools htmlwidgets httr igraph iml ipred isoband isotone iterators jquerylib jsonlite kernelshap kernlab KernSmooth kknn knitr labeling later lattice lava lazyeval libcoin lifecycle lime listenv lpSolve lpSolveAPI lubridate magrittr MASS Matrix MatrixModels matrixStats mda memoise Metrics mime minpack.lm ModelMetrics modeltools multcomp mvtnorm nlme nloptr nnet nnls np numDeriv openssl optiSolve otel parallelly party pillar pkgconfig plotly plotrix pls plyr polspline pROC prodlim progressr promises proxy PRROC purrr quadprog quantreg R6 randomForest rappdirs RColorBrewer Rcpp RcppArmadillo RcppEigen recipes reshape2 rgl rlang rlist rmarkdown rminer rms rpart rstudioapi S7 sandwich sass scales scatterplot3d shape shapes SparseM sparsevctrs SQUAREM stringi stringr strucchange survival sys TH.data tibble tidyr tidyselect timechange timeDate tinytex tzdb ucminf utf8 vctrs viridisLite withr writexl xfun xgboost XML yaml zoo

Vignettes and/or manual for this package failed to build, some documents may be unavailable. Please inspect thebuild logs for more information.

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

Package: PEAXAI 1.0.2

PEAXAI: Probabilistic Efficiency Analysis Using Explainable Artificial Intelligence

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