Package: boostingDEA 0.1.0

boostingDEA: A Boosting Approach to Data Envelopment Analysis

Includes functions to estimate production frontiers and make ideal output predictions in the Data Envelopment Analysis (DEA) context using both standard models from DEA and Free Disposal Hull (FDH) and boosting techniques. In particular, EATBoosting (Guillen et al., 2023 <doi:10.1016/j.eswa.2022.119134>) and MARSBoosting. Moreover, the package includes code for estimating several technical efficiency measures using different models such as the input and output-oriented radial measures, the input and output-oriented Russell measures, the Directional Distance Function (DDF), the Weighted Additive Measure (WAM) and the Slacks-Based Measure (SBM).

Authors:Maria D. Guillen [cre, aut], Juan Aparicio [aut], Víctor España [aut]

boostingDEA_0.1.0.tar.gz
boostingDEA_0.1.0.zip(r-4.7)boostingDEA_0.1.0.zip(r-4.6)boostingDEA_0.1.0.zip(r-4.5)
boostingDEA_0.1.0.tgz(r-4.6-any)boostingDEA_0.1.0.tgz(r-4.5-any)
boostingDEA_0.1.0.tar.gz(r-4.7-any)boostingDEA_0.1.0.tar.gz(r-4.6-any)
boostingDEA_0.1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
boostingDEA/json (API)

# Install 'boostingDEA' in R:

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

Bug tracker:https://github.com/itsmeryguillen/boostingdea/issues

Datasets:

banks - Taiwanese banks

On CRAN:

4.18 score 3 stars 4 scripts 249 downloads 14 exports 25 dependencies

Last updated from:acbe2c5149. Checks:9 OK. Indexed: yes.

Target	Result	Time
linux-devel-x86_64	OK	142
source / vignettes	OK	278
linux-release-x86_64	OK	182
macos-release-arm64	OK	102
macos-oldrel-arm64	OK	106
windows-devel	OK	113
windows-release	OK	131
windows-oldrel	OK	98
wasm-release	OK	110

Exports:bestEATBoost bestMARSBoost CobbDouglas DEA DEA_object EAT EATBoost EATBoost_object efficiency FDH FDH_object MARSAdapted MARSBoost MARSBoost_object

Dependencies:bitops caTools cli dplyr generics glue gplots gtools KernSmooth lifecycle lpSolveAPI magrittr MLmetrics pillar pkgconfig R6 Rglpk rlang ROCR slam tibble tidyselect utf8 vctrs withr

boostingDEA

Rendered fromboostingDEA.Rmdusingknitr::rmarkdown

Last update: 2023-05-15
Started: 2023-04-13

Help page	Topics
Add a new pair of Basis Functions	AddBF
Taiwanese banks (in 2010)	banks
Linear programming model for radial input measure	BBC_in
Linear programming model for radial output measure	BBC_out
Tuning an EATBoost model	bestEATBoost
Tuning an MARSBoost model	bestMARSBoost
Single Output Data Generation	CobbDouglas
Pareto-dominance relationships	comparePareto
Generate a new pair of Basis Functions	CreateBF
Generate a new pair of Cubic Basis Functions	CreateCubicBF
Linear programming model for Directional Distance Function measure	DDF
Data Envelope Analysis model	DEA DEA_object
Deep Efficiency Analysis Trees	deepEAT
Efficiency Analysis Trees	EAT
Create a EAT object	EAT_object
Gradient Tree Boosting	EATBoost EATBoost_object
Calculate efficiency scores	efficiency
Enhanced Russell Graph measure	ERG
Estimate Coefficients in Multivariate Adaptive Frontier Splines during Forward Procedure.	EstimCoeffsForward
Estimation of child nodes	estimEAT
Free Disposal Hull model	FDH FDH_object
Get 'EATBoost' leaves supports	get.a.EATBoost
Get the inferior corner of the leave support from all trees of 'EATBoost'	get.a.trees
Get the superior corner of the leave support from all trees of 'EATBoost'	get.b.trees
Get intersection between two leaves supports	get.intersection.a
Is Final Node	isFinalNode
Adapted Multivariate Adaptive Frontier Splines	MARSAdapted
Create an MARSAdapted object	MARSAdapted_object
Smoothing (Forward) Multivariate Adaptive Frontier Splines	MARSAdaptedSmooth
LS-Boosting with adapted Multivariate Adaptive Frontier Splines (MARS)	MARSBoost MARSBoost_object
Mean Squared Error	mse
Mean Squared Error	mse_tree
Position of the node	posIdNode
Model Prediction for DEA	predict.DEA
Model Prediction for Efficiency Analysis Trees.	predict.EAT
Model prediction for EATBoost algorithm	predict.EATBoost
Model Prediction for FDH	predict.FDH
Model Prediction for Adapted Multivariate Adaptive Frontier Splines.	predict.MARSAdapted
Model Prediction for Boosted Multivariate Adaptive Frontier Splines	predict.MARSBoost
Efficiency Analysis Trees Predictor	predictor
Data Pre-processing for Multivariate Adaptive Frontier Splines.	preProcess
Linear programming model for Russell input measure	Russell_in
Linear programming model for Russell output measure	Russell_out
Split node	split
Linear programming model for Weighted Additive Model	WAM

Package: boostingDEA 0.1.0

boostingDEA: A Boosting Approach to Data Envelopment Analysis

Citation

Development and contributors

Readme and manuals

Help Manual

Usage by other packages (reverse dependencies)