_            _    _        _         _
      /\ \         /\ \ /\ \     /\_\      / /\
      \_\ \       /  \ \\ \ \   / / /     / /  \
      /\__ \     / /\ \ \\ \ \_/ / /     / / /\ \__
     / /_ \ \   / / /\ \ \\ \___/ /     / / /\ \___\
    / / /\ \ \ / / /  \ \_\\ \ \_/      \ \ \ \/___/
   / / /  \/_// / /   / / / \ \ \        \ \ \
  / / /      / / /   / / /   \ \ \   _    \ \ \
 / / /      / / /___/ / /     \ \ \ /_/\__/ / /
/_/ /      / / /____\/ /       \ \_\\ \/___/ /
\_\/       \/_________/         \/_/ \_____\/
r-sfar 1.0.1
Propagated dependencies: r-ucminf@1.2.2 r-trustoptim@0.8.7.3 r-texreg@1.39.4 r-sandwich@3.1-1 r-randtoolbox@2.0.5 r-qrng@0.0-10 r-plm@2.6-6 r-nleqslv@3.3.5 r-mnorm@1.2.2 r-maxlik@1.5-2.1 r-marqlevalg@2.0.8 r-formula@1.2-5 r-fastghquad@1.0.1 r-cubature@2.1.3
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/hdakpo/sfaR
Licenses: GPL 3+
Synopsis: Stochastic Frontier Analysis Routines
Description:

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.

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