{ "_id": "6acdf492a14063b82c3ad7c1", "Package": "metANN", "Title": "Metaheuristic and Gradient-Based Optimization for Neural Network\nTraining and Continuous Problems", "Version": "0.1.0", "Authors@R": "c(\nperson(given = \"Burak\",\nfamily = \"Dilber\",\nemail = \"burakdilber91@gmail.com\",\nrole = c(\"aut\", \"cre\", \"cph\")),\nperson(given = \"A. Fırat\",\nfamily = \"Özdemir\",\nrole = c(\"aut\", \"ths\"))\n)", "Description": "Provides tools for general-purpose continuous optimization\nand feed-forward artificial neural network training using\nmetaheuristic and gradient-based optimization algorithms. The\npackage supports benchmark function optimization, regression,\nbinary classification, and multi-class classification with\nmultilayer perceptrons. The package implements several\noptimization methods, including particle swarm optimization\nKennedy and Eberhart (1995) ,\ndifferential evolution Storn and Price (1997)\n, grey wolf optimizer Mirjalili et\nal. (2014) , secretary\nbird optimization Fu et al. (2024)\n, and Adam Kingma and Ba (2015)\n.", "License": "MIT + file LICENSE", "Encoding": "UTF-8", "Roxygen": "list(markdown = TRUE)", "RoxygenNote": "7.3.3", "URL": "https://github.com/burakdilber/metANN", "BugReports": "https://github.com/burakdilber/metANN/issues", "Repository": "https://burakdilber.r-universe.dev", "Date/Publication": "2026-05-11 19:24:34 UTC", "RemoteUrl": "https://github.com/burakdilber/metann", "RemoteRef": "HEAD", "RemoteSha": "9fa689ff6ec12805ee5c24951e8b1bfed6030a4a", "NeedsCompilation": "no", "Packaged": { "Date": "2026-10-13 08:55:15 UTC", "User": "root" }, "Author": "Burak Dilber [aut, cre, cph],\nA. 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