Package: npsf 0.8.0
Oleg Badunenko
npsf: Nonparametric and Stochastic Efficiency and Productivity Analysis
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.
Authors:
npsf_0.8.0.tar.gz
npsf_0.8.0.tar.gz(r-4.5-noble)npsf_0.8.0.tar.gz(r-4.4-noble)
npsf_0.8.0.tgz(r-4.4-emscripten)npsf_0.8.0.tgz(r-4.3-emscripten)
npsf.pdf |npsf.html✨
npsf/json (API)
| # Install 'npsf' in R: |
| install.packages('npsf', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 4 years agofrom:87861539a7. Checks:OK: 2. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Oct 12 2024 |
| R-4.5-linux-x86_64 | OK | Oct 12 2024 |
Exports:haltonnptestindnptestrtsprimesrescalesftenonradialtenonradialbcteradialteradialbctruncreg
