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241 changes: 241 additions & 0 deletions joss.06424/10.21105.joss.06424.crossref.xml
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design in population kinetics. Computer Methods and Programs in
Biomedicine, 74.
https://doi.org/10.1016/s0169-2607(03)00073-7</unstructured_citation>
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imperialist competitive algorithm (ICA).
https://CRAN.R-project.org/package=ICAOD</unstructured_citation>
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<article_title>acebayes: An R package for Bayesian optimal
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