adea-package | R Documentation |

Package: adea Version: 1.5.1 Date: 2023-11-27 License: GPL (>= 3)

Data Envelopment Analysis (DEA) involves evaluating the efficiency of a set of Decision Making Units (DMUs) and calculating a relative efficiency score for each DMU. These scores are determined as a weighted ratio between all inputs and outputs amounts for such DMU.

DEA methodology assumes that all DMUs use the same set of inputs to produce the same set of outputs.

The selection of input and output variables for inclusion in a DEA model is a crucial aspect, as numerous studies have shown.
This package offers two variable selection procedures.
The first is ADEA, which is based on a measure of the relative importance of variables in the entire set of scores.
For more information on this methodology, see `adea`

.

An alternative way to select the variables for the model is by solving a mathematical optimization problem that determines the optimal selection based on some performance criteria.
`fsdea`

function provides an implementation of this procedure.

The main functions provided by this package are:

adea: Conducts ADEA analysis, providing efficiency scores for each DMU, a set of weights, and loads for each input and output variable, along with a model load.

adea_parametric: Does a stepwise analysis of removing variables step by steps. adea_hierarchical works in similar way.

fsea: Selects an optimal subset of input and output variables based on some performance criteria.

cadea: Performs Constrained ADEA analysis to enforce variable load constraints within a specified range, resulting in changes to efficiency scores.

adea_load_average: Identifies DMUs with a higher impact on the ADEA model.

This package is translation-ready, and contributions of translated versions of po files are highly welcome.

Fernando Fernandez-Palacin <fernando.fernandez@uca.es> and Manuel Munoz-Marquez <manuel.munoz@uca.es>

Mantainer: Manuel Munoz-Marquez <manuel.munoz@uca.es>

A new approach to the bi-dimensional representation of the DEA efficient frontier with multiple inputs and outputs.
*Carlos A. Bana e Costa* and *Joao Carlos C. B. Soares de Mello*, and *Lidia Angulo Meza*.
European Journal of Operational Research, 255 (1), pg. 175-186, 2016,
<DOI:10.1016/j.ejor.2016.05.012>.

Stepwise Selection of Variables in DEA Using Contribution Load.
*F. Fernandez-Palacin*, *M. A. Lopez-Sanchez*, and *M. Munoz-Marquez*.
Pesquisa Operacional 38 (1), pg. 1-24, 2018.
<DOI:10.1590/0101-7438.2018.038.01.0000>.

Feature Selection in Data Envelopment Analysis: A Mathematical Optimization approach.
*Benitez-Pena, S.*, *Bogetoft, P.*, and *Romero Morales, D.*.
Omega, Elsevier BV, 96, pp. 102068, 2020.
<DOI:10.1016/j.omega.2019.05.004>

Methodology for calculating critical values of relevance measures in variable selection methods in data envelopment analysis.
*Jeyms Villanueva-Cantillo* and *Manuel Munoz-Marquez*.
European Journal of Operational Research, 290 (2), pg. 657-670, 2021.
<DOI:10.1016/j.ejor.2020.08.021>.

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