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Fitting of non-parametric production frontiers for use in efficiency analysis. Methods are provided for both a smooth analogue of Data Envelopment Analysis (DEA) and a non-parametric analogue of Stochastic Frontier Analysis (SFA). Frontiers are constructed for multiple inputs and a single output using constrained kernel smoothing as in Racine et al. (2009), which allow for the imposition of monotonicity and concavity constraints on the estimated frontier.
| Version: | 0.0.1 |
| Depends: | R (≥ 3.5.0) |
| Imports: | abind (≥ 1.4.5), ggplot2 (≥ 3.1.0), prodlim (≥ 2018.4.18), quadprog (≥ 1.5.5), Rdpack (≥ 0.10.1), rootSolve (≥ 1.7) |
| Published: | 2018-12-01 |
| DOI: | 10.32614/CRAN.package.snfa |
| Author: | Taylor McKenzie [aut, cre] |
| Maintainer: | Taylor McKenzie <tkmckenzie at gmail.com> |
| License: | GPL-3 |
| NeedsCompilation: | no |
| CRAN checks: | snfa results |
| Reference manual: | snfa.pdf |
| Package source: | snfa_0.0.1.tar.gz |
| Windows binaries: | r-devel: snfa_0.0.1.zip, r-release: snfa_0.0.1.zip, r-oldrel: snfa_0.0.1.zip |
| macOS binaries: | r-release (arm64): snfa_0.0.1.tgz, r-oldrel (arm64): snfa_0.0.1.tgz, r-release (x86_64): snfa_0.0.1.tgz, r-oldrel (x86_64): snfa_0.0.1.tgz |
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