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SBOAtools: Secretary Bird Optimization for Continuous Optimization and Neural Network Training

Provides an implementation of Secretary Bird Optimization for general-purpose continuous optimization, benchmark optimization, and training single-hidden-layer feed-forward neural network models. The implemented optimizer is based on the Secretary Bird Optimization Algorithm proposed by Fu et al. (2024) <doi:10.1007/s10462-024-10729-y>. The neural network training functionality is based on Dilber and Özdemir (2026) <doi:10.1007/s00521-026-11874-x>.

Version: 0.1.1
Imports: stats, graphics
Published: 2026-05-03
DOI: 10.32614/CRAN.package.SBOAtools
Author: Burak Dilber [aut, cre, cph], A. Fırat Özdemir [aut, cph]
Maintainer: Burak Dilber <burakdilber91 at gmail.com>
BugReports: https://github.com/burakdilber/SBOAtools/issues
License: MIT + file LICENSE
URL: https://github.com/burakdilber/SBOAtools
NeedsCompilation: no
Materials: README, NEWS
CRAN checks: SBOAtools results

Documentation:

Reference manual: SBOAtools.html , SBOAtools.pdf

Downloads:

Package source: SBOAtools_0.1.1.tar.gz
Windows binaries: r-devel: SBOAtools_0.1.1.zip, r-release: SBOAtools_0.1.1.zip, r-oldrel: SBOAtools_0.1.1.zip
macOS binaries: r-release (arm64): SBOAtools_0.1.1.tgz, r-oldrel (arm64): SBOAtools_0.1.1.tgz, r-release (x86_64): SBOAtools_0.1.1.tgz, r-oldrel (x86_64): SBOAtools_0.1.1.tgz
Old sources: SBOAtools archive

Linking:

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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.