Package: npsf
Type: Package
Title: Nonparametric and Stochastic Efficiency and Productivity
        Analysis
Version: 0.8.0
Date: 2020-11-22
Author: Oleg Badunenko [aut, cre],
 Pavlo Mozharovskyi [aut],
 Yaryna Kolomiytseva [aut]
Maintainer: Oleg Badunenko <oleg.badunenko@brunel.ac.uk>
Description: 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.
Depends: Formula
Suggests: snowFT, Rmpi
Encoding: UTF-8
License: GPL-2
LinkingTo: Rcpp
NeedsCompilation: yes
Packaged: 2020-11-22 21:47:22 UTC; boo
Repository: CRAN
Date/Publication: 2020-11-22 22:10:02 UTC
Built: R 4.2.3; x86_64-w64-mingw32; 2024-04-24 00:57:21 UTC; windows
ExperimentalWindowsRuntime: ucrt
Archs: x64
