sfaR: Stochastic Frontier Analysis Routines
Maximum likelihood estimation for stochastic frontier
analysis (SFA) of production (profit) and cost functions. The package
includes the basic stochastic frontier for cross-sectional or pooled
data with several distributions for the one-sided error term (i.e.,
Rayleigh, gamma, Weibull, lognormal, uniform, generalized exponential
and truncated skewed Laplace), the latent class stochastic frontier
model (LCM) as described in Dakpo et al. (2021)
<doi:10.1111/1477-9552.12422>, for cross-sectional and pooled data,
and the sample selection model as described in Greene (2010)
<doi:10.1007/s11123-009-0159-1>, and applied in Dakpo et al. (2021)
<doi:10.1111/agec.12683>. Several possibilities in terms of
optimization algorithms are proposed.
| Version: |
1.0.1 |
| Depends: |
R (≥ 3.5.0) |
| Imports: |
cubature, fastGHQuad, Formula, marqLevAlg, maxLik, methods, mnorm, nleqslv, plm, qrng, randtoolbox, sandwich, stats, texreg, trustOptim, ucminf |
| Suggests: |
lmtest |
| Published: |
2024-10-29 |
| DOI: |
10.32614/CRAN.package.sfaR |
| Author: |
K Hervé Dakpo [aut, cre],
Yann Desjeux [aut],
Arne Henningsen [aut],
Laure Latruffe [aut] |
| Maintainer: |
K Hervé Dakpo <k-herve.dakpo at inrae.fr> |
| BugReports: |
https://github.com/hdakpo/sfaR/issues |
| License: |
GPL (≥ 3) |
| URL: |
https://github.com/hdakpo/sfaR |
| NeedsCompilation: |
no |
| Language: |
en-US |
| Citation: |
sfaR citation info |
| Materials: |
README NEWS |
| CRAN checks: |
sfaR results |
Documentation:
Downloads:
Reverse dependencies:
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