dsfa: Distributional Stochastic Frontier Analysis

Framework to fit distributional stochastic frontier models. Casts the stochastic frontier model into the flexible framework of distributional regression or otherwise known as General Additive Models of Location, Scale and Shape (GAMLSS). Allows for linear, non-linear, random and spatial effects on all the parameters of the distribution of the output, e.g. effects on the production or cost function, heterogeneity of the noise and inefficiency. Available distributions are the normal-halfnormal and normal-exponential distribution. Estimation via the fast and reliable routines of the 'mgcv' package. For more details see Schmidt R, Kneib T (2022) <doi:10.48550/arXiv.2208.10294>.

Version: 2.0.0
Depends: R (≥ 3.5.0)
Imports: mgcv, stats, Rdpack, Rcpp, RcppArmadillo, copula, gratia
LinkingTo: Rcpp, RcppArmadillo
Suggests: plm
Published: 2023-02-03
Author: Rouven Schmidt [aut, cre]
Maintainer: Rouven Schmidt <rouven.schmidt at tu-clausthal.de>
License: MIT + file LICENSE
NeedsCompilation: yes
SystemRequirements: C++17
Materials: NEWS
CRAN checks: dsfa results

Documentation:

Reference manual: dsfa.pdf

Downloads:

Package source: dsfa_2.0.0.tar.gz
Windows binaries: r-devel: dsfa_1.0.1.zip, r-release: dsfa_1.0.1.zip, r-oldrel: dsfa_1.0.1.zip
macOS binaries: r-release (arm64): dsfa_1.0.1.tgz, r-oldrel (arm64): dsfa_1.0.1.tgz, r-release (x86_64): dsfa_1.0.1.tgz, r-oldrel (x86_64): dsfa_1.0.1.tgz
Old sources: dsfa archive

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