https://burakdilber.r-universe.devRecent package updates in burakdilberR-universehttps://github.com/burakdilber.pnghttps://burakdilber.r-universe.devFri, 15 May 2026 21:31:54 GMTburakdilber91@gmail.com (Burak Dilber)Provides tools for general-purpose continuous optimization and feed-forward artificial neural network training using metaheuristic and gradient-based optimization algorithms. The package supports benchmark function optimization, regression, binary classification, and multi-class classification with multilayer perceptrons. The package implements several optimization methods, including particle swarm optimization Kennedy and Eberhart (1995) <doi:10.1109/ICNN.1995.488968>, differential evolution Storn and Price (1997) <doi:10.1023/A:1008202821328>, grey wolf optimizer Mirjalili et al. (2014) <doi:10.1016/j.advengsoft.2013.12.007>, secretary bird optimization Fu et al. (2024) <doi:10.1007/s10462-024-10729-y>, and Adam Kingma and Ba (2015) <doi:10.48550/arXiv.1412.6980>.https://github.com/r-universe/cran/actions/runs/25943460641Fri, 15 May 2026 21:31:54 GMTmetANN0.1.0successhttps://cran.r-universe.devhttps://github.com/cran/metANNburakdilber91@gmail.com (Burak Dilber)Provides tools for general-purpose continuous optimization and feed-forward artificial neural network training using metaheuristic and gradient-based optimization algorithms. The package supports benchmark function optimization, regression, binary classification, and multi-class classification with multilayer perceptrons. The package implements several optimization methods, including particle swarm optimization Kennedy and Eberhart (1995) <doi:10.1109/ICNN.1995.488968>, differential evolution Storn and Price (1997) <doi:10.1023/A:1008202821328>, grey wolf optimizer Mirjalili et al. (2014) <doi:10.1016/j.advengsoft.2013.12.007>, secretary bird optimization Fu et al. (2024) <doi:10.1007/s10462-024-10729-y>, and Adam Kingma and Ba (2015) <doi:10.48550/arXiv.1412.6980>.https://github.com/r-universe/burakdilber/actions/runs/25956883950Mon, 11 May 2026 19:24:34 GMTmetANN0.1.0successhttps://burakdilber.r-universe.devhttps://github.com/burakdilber/metannburakdilber91@gmail.com (Burak Dilber)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>.https://github.com/r-universe/burakdilber/actions/runs/26740188272Sat, 02 May 2026 15:10:12 GMTSBOAtools0.1.1successhttps://burakdilber.r-universe.devhttps://github.com/burakdilber/sboatools