The data the model was introduced on | A technology with a known answer | Radial efficiency | Slacks-based efficiency | Directional distance | Productivity over time | Scale | The part most DEA analyses leave out | How fast can any of this converge? | Ranking the units that tied | When prices are known | The weights themselves
Package: DEA 1.0.3
David Bernstein
DEA: Data Envelopment Analysis
Nonparametric efficiency measurement by data envelopment analysis. Provides radial (Charnes-Cooper-Rhodes and Banker-Charnes-Cooper) technical efficiency under constant, variable, non-increasing and non-decreasing returns to scale, the slacks-based measure of Tone (2001), the additive model of Charnes and others (1985), and the directional distance function of Chambers, Chung and Fare (1996), all through one interface and one result object. Efficiency estimates are accompanied by peers, slacks, returns-to-scale classification, scale efficiency and the optimal multipliers, and by bias-corrected estimates and confidence intervals from the smoothed homogeneous bootstrap of Simar and Wilson (1998), which also supplies a test of constant returns to scale for the sample as a whole (Simar and Wilson, 2002). Where prices are known, cost, revenue and Nerlovian profit efficiency separate the technical component from the allocative one; where they are not, cross-efficiency with the secondary goals of Doyle and Green (1994) ranks units that a self-appraisal leaves tied. This package succeeds the archived 'DEA' package of Diaz-Martinez and Fernandez-Menendez (2008).
Authors:
DEA_1.0.3.tar.gz
DEA_1.0.3.zip(r-4.7-any)DEA_1.0.3.zip(r-4.6-any)DEA_1.0.3.zip(r-4.5-any)
DEA_1.0.3.tgz(r-4.6-any)DEA_1.0.3.tgz(r-4.5-any)
DEA_1.0.3.tar.gz(r-4.7-any)DEA_1.0.3.tar.gz(r-4.6-any)
DEA_1.0.3.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
DEA/json (API)
| # Install 'DEA' in R: |
| install.packages('DEA', repos = c('https://davidhbernstein.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/davidhbernstein/dea/issues
Datasets:
- charnes1981 - Program Follow Through: the original data envelopment analysis data set
Last updated from:e2c20717ab. Checks:9 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel | OK | 168 | ||
| source / vignettes | OK | 244 | ||
| linux-release | OK | 137 | ||
| macos-release | OK | 159 | ||
| macos-oldrel | OK | 129 | ||
| windows-devel | OK | 121 | ||
| windows-release | OK | 129 | ||
| windows-oldrel | OK | 130 | ||
| wasm-release | OK | 130 |
Exports:deadea_adddea_bootdea_categoricaldea_conedea_costdea_crossdea_ddfdea_malmquistdea_multdea_nddea_paneldea_profitdea_ratedea_regdea_revenuedea_rtsdea_rts_testdea_sbmdea_simdea_subsampledea_undesirabledea_weakdea_wrefficiencymultiplierspeersslackswr_boundwr_linearwr_ratio
Dependencies:lpSolveAPI
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Data Envelopment Analysis | DEA-package |
| Program Follow Through: the original data envelopment analysis data set | charnes1981 |
| Radial technical efficiency | dea |
| Additive efficiency, and the weighted measures built on it | dea_add |
| Bias correction and confidence intervals by the Simar-Wilson bootstrap | dea_boot plot.dea_boot print.dea_boot summary.dea_boot |
| Categorical environmental variables, as nested reference sets | dea_categorical print.dea_categorical summary.dea_categorical |
| Cost, revenue and profit efficiency | dea_cost dea_profit dea_revenue efficiency.dea_price fitted.dea_price nobs.dea_price peers.dea_price plot.dea_price print.dea_price summary.dea_price |
| Cross-efficiency | dea_cross efficiency.dea_cross nobs.dea_cross plot.dea_cross print.dea_cross summary.dea_cross |
| Directional distance function | dea_ddf |
| Panel data and the Malmquist productivity index | dea_malmquist dea_panel print.dea_malmquist print.dea_panel summary.dea_malmquist summary.dea_panel |
| The multiplicative, or piecewise Cobb-Douglas, model | dea_mult print.dea_mult summary.dea_mult |
| Non-discretionary inputs and outputs | dea_nd print.dea_nd |
| Two-stage efficiency regression (Simar and Wilson 2007) | dea_reg print.dea_reg summary.dea_reg |
| Scale efficiency and returns to scale | dea_rts plot.dea_rts print.dea_rts summary.dea_rts |
| Nonparametric test of returns to scale | dea_rts_test print.dea_rts_test summary.dea_rts_test |
| Slacks-based measure of efficiency | dea_sbm |
| Simulate a technology with known efficiency, and the rate it can be estimated at | dea_rate dea_sim plot.dea_sim print.dea_sim |
| Confidence intervals by the m out of n bootstrap | dea_subsample print.dea_subsample summary.dea_subsample |
| Efficiency with undesirable outputs | dea_undesirable print.dea_undesirable |
| Weak disposability of undesirable outputs | dea_weak print.dea_weak |
| Weight restrictions, assurance regions and the cone-ratio model | c.dea_restriction dea_cone dea_wr print.dea_restriction print.dea_wr wr_bound wr_linear wr_ratio |
| Methods for objects of class "dea" | efficiency efficiency.dea fitted.dea multipliers multipliers.dea multipliers.dea_cross nobs.dea peers peers.dea plot.dea print.dea slacks slacks.dea summary.dea |