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.html , 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

Reverse dependencies:

Reverse imports:

snreg

Linking:

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