FEAR

FEAR: Frontier Efficiency Analysis with R. FEAR consists of a software library that can be linked to the general-purpose statistical package R. The routines included in FEAR allow the user to compute, among other things, nonparametric estimates of technical, allocative, and overall efficiency while assuming either variable, non-increasing, or constant returns to scale. The routines are highly flexible, allowing measurement of efficiency of one group of observations relative to a technology defined by a second, reference group of observations. Consequently, the routines can be used to compute estimates of Malmquist indices, scale efficiency measures, super-efficiency scores, and other measures that might be of interest. Before using FEAR, one must download and install R. The R installation program and documentation for R can be found at the R website. R is distributed under the GNU General Public License Version 2. FEAR is distributed under the license that appears in the file named ”LICENSE” that is part of the FEAR package; click here to see the license before downloading the software or documentation. Downloading the FEAR software or documentation constitutes acceptance of the terms and conditions in the license.


References in zbMATH (referenced in 10 articles )

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  1. Lindlbauer, Ivonne; Schreyögg, Jonas; Winter, Vera: Changes in technical efficiency after quality management certification: A DEA approach using difference-in-difference estimation with genetic matching in the hospital industry (2016)
  2. Matousek, Roman; Tzeremes, Nickolaos G.: CEO compensation and bank efficiency: an application of conditional nonparametric frontiers (2016)
  3. Krüger, Jens J.: A Monte Carlo study of old and new frontier methods for efficiency measurement (2012)
  4. Tortosa-Ausina, Emili; Armero, Carmen; Conesa, David; Grifell-Tatjé, Emili: Bootstrapping profit change: an application to Spanish banks (2012)
  5. Bogetoft, Peter; Otto, Lars: Benchmarking with DEA, SFA, and R. (2011)
  6. Ramilan, Thiagarajah; Scrimgeour, Frank; Marsh, Dan: Analysis of environmental and economic efficiency using a farm population micro-simulation model (2011)
  7. da Silva E.Souza, Geraldo; Gomes, Eliane Gonçalves; Staub, Roberta Blass: Probabilistic measures of efficiency and the influence of contextual variables in nonparametric production models: an application to agricultural research in Brazil (2010)
  8. Chen, Wen-Chih; Cho, Wei-Jen: A procedure for large-scale DEA computations (2009)
  9. Daraio, Cinzia; Simar, Léopold: Advanced robust and nonparametric methods in efficiency analysis. Methodology and applications (2007)
  10. Simar, Léopold; Wilson, Paul W.: Performance of the bootstrap for DEA estimators and iterating the principle (2004)