{ "_id": "6a1f1f39b401979e734200f0", "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": "2026-05-16 08:07:14 UTC", "User": "root" }, "Author": "Burak Dilber [aut, cre, cph],\nA. 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"title": "Convert Character Input to an Activation Object", "topics": [ "as_activation" ] }, { "page": "as_loss", "title": "Convert Character Input to a Loss Object", "topics": [ "as_loss" ] }, { "page": "as_metric", "title": "Convert Character Input to a Metric Object", "topics": [ "as_metric" ] }, { "page": "as_metrics", "title": "Convert Multiple Inputs to Metric Objects", "topics": [ "as_metrics" ] }, { "page": "as_optimizer", "title": "Convert Character Input to an Optimizer Object", "topics": [ "as_optimizer" ] }, { "page": "available_activations", "title": "List Available Activation Functions", "topics": [ "available_activations" ] }, { "page": "available_gradient_optimizers", "title": "List Available Gradient-Based Optimizers", "topics": [ "available_gradient_optimizers" ] }, { "page": "available_losses", "title": "List Available Loss Functions", "topics": [ "available_losses" ] }, { "page": "available_metaheuristics", "title": "List Available Metaheuristic Optimizers", "topics": [ "available_metaheuristics" ] }, { "page": "available_metrics", "title": "List Available Performance Metrics", "topics": [ "available_metrics" ] }, { "page": "available_optimizers", "title": "List Available Optimizers", "topics": [ "available_optimizers" ] }, { "page": "coef.met_optimize_result", "title": "Extract the Best Parameters from a metANN Optimization Result", "topics": [ "coef.met_optimize_result" ] }, { "page": "coef.metann", "title": "Extract Weights from a metANN Model", "topics": [ "coef.metann" ] }, { "page": "count_parameters", "title": "Count the Number of Trainable Parameters in an MLP Architecture", "topics": [ "count_parameters" ] }, { "page": "decode_weights", "title": "Decode an MLP Weight Vector", "topics": [ "decode_weights" ] }, { "page": "dense_layer", "title": "Create a Dense Layer", "topics": [ "dense_layer" ] }, { "page": "evaluate", "title": "Evaluate a metANN Model", "topics": [ "evaluate" ] }, { "page": "forward_pass", "title": "Forward Pass 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Whether an Object is a metANN Optimizer", "topics": [ "is_optimizer" ] }, { "page": "loss_binary_crossentropy", "title": "Binary Cross-Entropy Loss", "topics": [ "loss_binary_crossentropy" ] }, { "page": "loss_crossentropy", "title": "Categorical Cross-Entropy Loss", "topics": [ "loss_crossentropy" ] }, { "page": "loss_huber", "title": "Huber Loss", "topics": [ "loss_huber" ] }, { "page": "loss_log_cosh", "title": "Log-Cosh Loss", "topics": [ "loss_log_cosh" ] }, { "page": "loss_mae", "title": "Mean Absolute Error Loss", "topics": [ "loss_mae" ] }, { "page": "loss_mse", "title": "Mean Squared Error Loss", "topics": [ "loss_mse" ] }, { "page": "met_mlp", "title": "Train a Feed-Forward Multilayer Perceptron", "topics": [ "met_mlp" ] }, { "page": "met_optimize", "title": "General-Purpose Optimization", "topics": [ "met_optimize" ] }, { "page": "metann", "title": "Train an Artificial Neural Network with metANN", "topics": [ "metann" ] }, { "page": "metric_accuracy", "title": "Accuracy 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