sfaR: Stochastic Frontier Analysis using R

Maximum likelihood estimation for stochastic frontier analysis (SFA) of production (profit) and cost functions. The package includes several distributions for the one-sided error term (i.e. Rayleigh, Gamma, Weibull, lognormal, uniform, generalized exponential and truncated skewed Laplace) as well as the latent class stochastic frontier model (LCM) as described in Dakpo et al. (2021) <doi:10.1111/1477-9552.12422>. Several possibilities in terms of optimization algorithms are proposed.

Version: 0.1.1
Depends: R (≥ 3.5.0)
Imports: dplyr, emdbook, fBasics, Formula, gsl, marqLevAlg, MASS, maxLik, methods, moments, nleqslv, numDeriv, primes, qrng, randtoolbox, trustOptim, ucminf
Suggests: mlogit
Published: 2022-05-05
Author: K Hervé Dakpo [aut], Yann Desjeux [aut, cre], Laure Latruffe [aut]
Maintainer: Yann Desjeux <yann.desjeux at inrae.fr>
BugReports: https://r-forge.r-project.org/tracker/?group_id=2413
License: GPL-3
NeedsCompilation: no
Citation: sfaR citation info
Materials: NEWS
CRAN checks: sfaR results


Reference manual: sfaR.pdf


Package source: sfaR_0.1.1.tar.gz
Windows binaries: r-devel: sfaR_0.1.1.zip, r-release: sfaR_0.1.1.zip, r-oldrel: sfaR_0.1.1.zip
macOS binaries: r-release (arm64): sfaR_0.1.1.tgz, r-oldrel (arm64): sfaR_0.1.1.tgz, r-release (x86_64): sfaR_0.1.1.tgz, r-oldrel (x86_64): sfaR_0.1.1.tgz
Old sources: sfaR archive

Reverse dependencies:

Reverse imports: micEconDistRay
Reverse suggests: dsfa


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