Package: snfa 0.0.1.9000

snfa: Smooth Non-Parametric Frontier Analysis

Fitting of non-parametric production frontiers for use in efficiency analysis. Methods are provided for both a smooth analogue of Data Envelopment Analysis (DEA) and a non-parametric analogue of Stochastic Frontier Analysis (SFA). Frontiers are constructed for multiple inputs and a single output using constrained kernel smoothing as in Racine et al. (2009), which allow for the imposition of monotonicity and concavity constraints on the estimated frontier.

Authors:Taylor McKenzie [aut, cre]

snfa_0.0.1.9000.tar.gz


snfa_0.0.1.9000.tar.gz(r-4.4-noble)
snfa_0.0.1.9000.tgz(r-4.4-emscripten)snfa_0.0.1.9000.tgz(r-4.3-emscripten)
snfa.pdf |snfa.html✨
snfa/json (API)

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

Peer review:

Bug tracker:https://github.com/tkmckenzie/snfa/issues

Datasets:

On CRAN:

8 exports 0.62 score 24 dependencies 8 scripts 130 downloads

Last updated 4 years agofrom:457097e7f2. Checks:OK: 1. Indexed: yes.

TargetResultDate
Doc / VignettesOKAug 28 2024

Exports:allocative.efficiencydeafit.boundaryfit.meanfit.sfH.inv.selectreflect.datatechnical.efficiency.change

Dependencies:abindclicodetoolsdata.tablediagramdigestfuturefuture.applyglobalsKernSmoothlatticelavalistenvMatrixnumDerivparallellyprodlimprogressrquadprogRcpprootSolveshapeSQUAREMsurvival

Introduction to snfa

Rendered fromsnfa.Rmdusingknitr::rmarkdownon Aug 28 2024.

Last update: 2020-06-10
Started: 2018-12-03