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  "Title": "Evolutionary Mode Jumping Markov Chain Monte Carlo Expert\nToolbox",
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  "Date": "2024-05-02",
  "Authors@R": "c(\nperson(\"Aliaksandr\", \"Hubin\", email = \"aliaksah@math.uio.no\", role = c(\"aut\")),\nperson(\"Waldir\", \"Leoncio\", email = \"w.l.netto@medisin.uio.no\", role = c(\"cre\",\"aut\")),\nperson(\"Geir\", \"Storvik\", role = c(\"ctb\")),\nperson(\"Florian\", \"Frommlet\", role = c(\"ctb\"))\n)",
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  "Description": "Implementation of the Mode Jumping Markov Chain Monte\nCarlo algorithm from Hubin, A., Storvik, G. (2018)\n<doi:10.1016/j.csda.2018.05.020>, Genetically Modified Mode\nJumping Markov Chain Monte Carlo from Hubin, A., Storvik, G., &\nFrommlet, F. (2020) <doi:10.1214/18-BA1141>, Hubin, A.,\nStorvik, G., & Frommlet, F. (2021) <doi:10.1613/jair.1.13047>,\nand Hubin, A., Heinze, G., & De Bin, R. (2023)\n<doi:10.3390/fractalfract7090641>, and Reversible Genetically\nModified Mode Jumping Markov Chain Monte Carlo from Hubin, A.,\nFrommlet, F., & Storvik, G. (2021)\n<doi:10.48550/arXiv.2110.05316>, which allow for estimating\nposterior model probabilities and Bayesian model averaging\nacross a wide set of Bayesian models including linear,\ngeneralized linear, generalized linear mixed, generalized\nnonlinear, generalized nonlinear mixed, and logic regression\nmodels.",
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