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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
DOI: 10.32614/CRAN.package.npsf
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

Documentation:

Reference manual: npsf.pdf

Downloads:

Package source: npsf_0.8.0.tar.gz
Windows binaries: r-devel: npsf_0.8.0.zip, r-release: npsf_0.8.0.zip, r-oldrel: npsf_0.8.0.zip
macOS binaries: r-release (arm64): npsf_0.8.0.tgz, r-oldrel (arm64): npsf_0.8.0.tgz, r-release (x86_64): npsf_0.8.0.tgz, r-oldrel (x86_64): npsf_0.8.0.tgz
Old sources: npsf archive

Linking:

Please use the canonical form https://CRAN.R-project.org/package=npsf to link to this page.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.