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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).
| Version: |
0.1.0 |
| Depends: |
R (≥ 3.5.0) |
| Imports: |
Rglpk, dplyr, lpSolveAPI, stats, MLmetrics, methods |
| Suggests: |
knitr, rmarkdown, testthat (≥ 3.0.0) |
| Published: |
2023-05-15 |
| DOI: |
10.32614/CRAN.package.boostingDEA |
| Author: |
Maria D. Guillen 
[cre, aut],
Juan Aparicio
[aut],
Víctor España
[aut] |
| Maintainer: |
Maria D. Guillen <maria.guilleng at umh.es> |
| BugReports: |
https://github.com/itsmeryguillen/boostingDEA/issues |
| License: |
AGPL (≥ 3) |
| URL: |
https://github.com/itsmeryguillen/boostingDEA |
| NeedsCompilation: |
no |
| Materials: |
README |
| CRAN checks: |
boostingDEA results |
Documentation:
Downloads:
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.