Package: Benchmarking 0.32

Benchmarking: Benchmark and Frontier Analysis Using DEA and SFA

Methods for frontier analysis, Data Envelopment Analysis (DEA), under different technology assumptions (fdh, vrs, drs, crs, irs, add/frh, and fdh+), and using different efficiency measures (input based, output based, hyperbolic graph, additive, super, and directional efficiency). Peers and slacks are available, partial price information can be included, and optimal cost, revenue and profit can be calculated. Evaluation of mergers is also supported. Methods for graphing the technology sets are also included. There is also support for comparative methods based on Stochastic Frontier Analyses (SFA) and for convex nonparametric least squares of convex functions (STONED). In general, the methods can be used to solve not only standard models, but also many other model variants. It complements the book, Bogetoft and Otto, Benchmarking with DEA, SFA, and R, Springer-Verlag, 2011, but can of course also be used as a stand-alone package.

Authors:Peter Bogetoft and Lars Otto

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Benchmarking.pdf |Benchmarking.html
Benchmarking/json (API)
NEWS

# Install 'Benchmarking' in R:
install.packages('Benchmarking', repos = c('https://larsot23.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Uses libs:
  • c++– GNU Standard C++ Library v3
Datasets:

On CRAN:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

5.84 score 7 stars 7 packages 180 scripts 2.6k downloads 22 mentions 58 exports 4 dependencies

Last updated 8 months agofrom:0b84053f6a. Checks:OK: 7 NOTE: 2. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 14 2024
R-4.5-win-x86_64NOTENov 14 2024
R-4.5-linux-x86_64NOTENov 14 2024
R-4.4-win-x86_64OKNov 14 2024
R-4.4-mac-x86_64OKNov 14 2024
R-4.4-mac-aarch64OKNov 14 2024
R-4.3-win-x86_64OKNov 14 2024
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Exports:boot.fearcost.optcritValuedeadea.adddea.bootdea.directdea.dualdea.mergedea.plotdea.plot.frontierdea.plot.isoquantdea.plot.transformeffeff.denseff.dens.plotefficiencieseladdereladder.ploteladder2excessget.number.peersget.peers.lambdaget.which.peerslambdalambda.printlambda.sfamake.mergemalmqmalmquistmeamea.linesoutlier.apoutlier.ap.plotoutlierC.appeersprint.cost.optprint.profit.optprint.revenue.optprofit.optrevenue.optsdeasfasfa.costsigma2.sfasigma2u.sfasigma2v.sfaslackstonedsummary.cost.optsummary.profit.optsummary.revenue.optte.add.sfate.sfateBC.sfateJ.sfateMode.sfatypeIerror

Dependencies:lpSolveAPIquadprogRcppucminf

Readme and manuals

Help Manual

Help pageTopics
Data Envelopment Analyses (DEA) and Stochastic Frontier Analyses (SFA) - Model Estimations and Efficiency MeasuringBenchmarking-package Benchmarking
Data: Charnes et al. (1981): Program follow throughcharnes1981
DEA optimal cost, revenue, and profitcost.opt print.cost.opt print.profit.opt print.revenue.opt profit.opt revenue.opt summary.cost.opt summary.profit.opt summary.revenue.opt
Critical values from bootstrapped DEA modelscritValue
DEA efficiencydea print.Farrell summary.Farrell
Additive DEA modeldea.add
Bootstrap DEA modelsboot.fear dea.boot
Directional efficiencydea.direct
Dual DEA models and assurance regionsdea.dual
Estimate potential merger gains and their decompositionsdea.merge
Plot of DEA technologiesdea.plot dea.plot.frontier dea.plot.isoquant dea.plot.transform
Calculate efficiencies for Farrell and sfa objecteff eff.Farrell eff.sfa efficiencies efficiencies.default efficiencies.Farrell efficiencies.sfa
Estimate and plot density of efficiencieseff.dens eff.dens.plot
Efficiency ladder for a single firmeladder eladder.plot eladder2
Excess input compared over frontier inputexcess
Lambdas or the weight of the peerslambda lambda.print
Make an aggregation matrix to perform mergersmake.merge
Malmquist indexmalmq
Malmquist index for firms in a panelmalmquist
MEA multi-directional efficiency analysismea mea.lines
Data: Milk producersmilkProd
Data: Forestry in NorwaynorWood2004
Detection of outliers in benchmark modelsoutlier.ap outlier.ap.plot outlierC.ap
Find peer firms and unitsget.number.peers get.peers.lambda get.which.peers peers
Data: Multi-output pig producerspigdata
Data: Milk producersprojekt
Super efficiencysdea
Stochastic frontier estimationcoef.sfa lambda.sfa logLik.sfa print.sfa residuals.sfa sfa sfa.cost sigma2.sfa sigma2u.sfa sigma2v.sfa summary.sfa te.add.sfa te.sfa teBC.sfa teJ.sfa teMode.sfa
Calculate slack in an efficiency analysisprint.slack slack summary.slack
Convex nonparametric least squaresstoned
Probability of type I error for test in a bootstrap DEA modeltypeIerror