Package: spscsfa 0.1.0

spscsfa: Semiparametric Smooth-Coefficient Stochastic Frontier Analysis

Provides semiparametric smooth-coefficient stochastic frontier analysis following Sun and Kumbhakar (2013) <doi:10.1016/j.econlet.2013.05.001> where the coefficients of the parametric part vary smoothly with a set of nonparametric variables. Inefficiency term is allowed to depend on a set of determinants through heteroskedasticity. Smooth coefficients are estimated using nonparametric regression and the remaining frontier parameters are estimated by maximum likelihood. Technical efficiency and inefficiency are computed using the Battese and Coelli (1988) <doi:10.1016/0304-4076(88)90053-X> and Jondrow et al. (1982) <doi:10.1016/0304-4076(82)90004-5> methods, respectively. Confidence intervals for technical efficiency are computed using the approach of Horrace and Schmidt (1996) <doi:10.1007/BF00157044>.

Authors:Kai Sun [aut, cre]

spscsfa_0.1.0.tar.gz
spscsfa_0.1.0.zip(r-4.7-any)spscsfa_0.1.0.zip(r-4.6-any)spscsfa_0.1.0.zip(r-4.5-any)
spscsfa_0.1.0.tgz(r-4.6-any)spscsfa_0.1.0.tgz(r-4.5-any)
spscsfa_0.1.0.tar.gz(r-4.7-any)spscsfa_0.1.0.tar.gz(r-4.6-any)
spscsfa_0.1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
spscsfa/json (API)

# Install 'spscsfa' in R:
install.packages('spscsfa', repos = c('https://kaisun84.r-universe.dev', 'https://cloud.r-project.org'))
Datasets:

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

1.00 score 4 exports 14 dependencies

Last updated from:93adf5b200. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develOK146
source / vignettesOK163
linux-releaseOK155
macos-releaseOK199
macos-oldrelOK197
windows-develOK81
windows-releaseOK81
windows-oldrelOK81
wasm-releaseOK106

Exports:inefficiencysmooth_coefspsc_sfate

Dependencies:bootcrscubatureFormulalatticeMASSMatrixMatrixModelsnpquadprogquantregRcppSparseMsurvival