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.

Version: 0.8.0
Depends: Formula
LinkingTo: Rcpp
Suggests: snowFT, Rmpi
Published: 2020-11-22
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

Downloads:

Reference manual: npsf.pdf
Package source: npsf_0.8.0.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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