Nonparametric efficiency measurement and statistical inference via DEA type estimators (see Färe, Grosskopf, and Lovell (1994) <doi:10.1017/CBO9780511551710>, Kneip, Simar, and Wilson (2008) <doi:10.1017/S0266466608080651> and Badunenko and Mozharovskyi (2020) <doi:10.1080/01605682.2019.1599778>) as well as Stochastic Frontier estimators for both cross-sectional data and 1st, 2nd, and 4th generation models for panel data (see Kumbhakar and Lovell (2003) <doi:10.1017/CBO9781139174411>, Badunenko and Kumbhakar (2016) <doi:10.1016/j.ejor.2016.04.049>). The stochastic frontier estimators can handle both half-normal and truncated normal models with conditional mean and heteroskedasticity. The marginal effects of determinants can be obtained.
| Version: | 0.7.1 |
| Depends: | Formula |
| LinkingTo: | Rcpp |
| Suggests: | snowFT, Rmpi |
| Published: | 2020-06-24 |
| Author: | Oleg Badunenko [aut, cre], Pavlo Mozharovskyi [aut], Yaryna Kolomiytseva [aut] |
| Maintainer: | Oleg Badunenko <oleg.badunenko at brunel.ac.uk> |
| License: | GPL-2 |
| NeedsCompilation: | yes |
| Materials: | ChangeLog |
| CRAN checks: | npsf results |
| Reference manual: | npsf.pdf |
| Package source: | npsf_0.7.1.tar.gz |
| Windows binaries: | r-devel: npsf_0.7.1.zip, r-release: npsf_0.7.1.zip, r-oldrel: npsf_0.7.1.zip |
| macOS binaries: | r-release: npsf_0.7.1.tgz, r-oldrel: npsf_0.7.1.tgz |
| Old sources: | npsf archive |
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