eaf: Plots of the Empirical Attainment Function

Computation and visualization of the empirical attainment function (EAF) for the analysis of random sets in multi-criterion optimization. M. López-Ibáñez, L. Paquete, and T. Stützle (2010) <doi:10.1007/978-3-642-02538-9_9>.

Version: 2.0
Depends: R (≥ 3.2)
Imports: modeltools, graphics, grDevices, stats, Rdpack
Suggests: testthat, withr, covr
Published: 2021-02-09
Author: Manuel López-Ibáñez ORCID iD [aut, cre], Marco Chiarandini [aut], Carlos Fonseca [aut], Luís Paquete [aut], Thomas Stützle [aut], Mickaël Binois [ctb]
Maintainer: Manuel López-Ibáñez <manuel.lopez-ibanez at manchester.ac.uk>
BugReports: https://github.com/MLopez-Ibanez/eaf/issues
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL: http://lopez-ibanez.eu/eaftools, https://github.com/MLopez-Ibanez/eaf
NeedsCompilation: yes
SystemRequirements: GNU make, Gnu Scientific Library
Citation: eaf citation info
Materials: README NEWS
CRAN checks: eaf results


Reference manual: eaf.pdf
Package source: eaf_2.0.tar.gz
Windows binaries: r-devel: eaf_2.0.zip, r-release: eaf_2.0.zip, r-oldrel: eaf_1.9-1.zip
macOS binaries: r-release: eaf_2.0.tgz, r-oldrel: eaf_2.0.tgz
Old sources: eaf archive

Reverse dependencies:

Reverse suggests: ParamHelpers


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