Provides estimation procedures for copula-based stochastic frontier quantile models for cross-sectional data. The package implements maximum likelihood estimation of quantile regression models allowing flexible dependence structures between error components through various copula families (e.g., Gaussian and Student-t). It enables estimation of conditional quantile effects, dependence parameters, log-likelihood values, and information criteria (AIC and BIC). The framework combines quantile regression methodology introduced by Koenker and Bassett (1978) <doi:10.2307/1913643> with copula theory described in Joe (2014, ISBN:9781466583221). This approach allows modeling heterogeneous effects across quantiles while capturing nonlinear dependence structures between variables.
| Version: | 0.1.0 |
| Imports: | ald, VineCopula, stats, graphics, MASS |
| Published: | 2026-03-04 |
| DOI: | 10.32614/CRAN.package.copulaSQM |
| Author: | Woraphon Yamaka [aut, cre], Paravee Maneejuk [aut], Nuttaphong Kaewtathip [aut] |
| Maintainer: | Woraphon Yamaka <woraphon.econ at gmail.com> |
| License: | GPL-3 |
| NeedsCompilation: | no |
| Materials: | README |
| CRAN checks: | copulaSQM results |
| Reference manual: | copulaSQM.html , copulaSQM.pdf |
| Package source: | copulaSQM_0.1.0.tar.gz |
| Windows binaries: | r-devel: copulaSQM_0.1.0.zip, r-release: copulaSQM_0.1.0.zip, r-oldrel: copulaSQM_0.1.0.zip |
| macOS binaries: | r-release (arm64): copulaSQM_0.1.0.tgz, r-oldrel (arm64): copulaSQM_0.1.0.tgz, r-release (x86_64): copulaSQM_0.1.0.tgz, r-oldrel (x86_64): copulaSQM_0.1.0.tgz |
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