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  "Title": "A Deep Dynamic Experimental Self-Organizing Neural Network\nFramework",
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  "Description": "Provides a fully native R deep learning framework for\nconstructing, training, evaluating, and inspecting Deep Dynamic\nEnsemble Self Organizing Neural Networks at research scale. The\ncore engine is an object oriented R6 class-based implementation\nwith explicit control over layer layout, dimensional flow,\nforward propagation, back propagation, and transparent\noptimizer state updates. The framework does not rely on\nexternal deep learning back ends, enabling direct inspection of\nmodel state, reproducible numerical behavior, and fine grained\narchitectural control without requiring compiled dependencies\nor graphics processing unit specific run times. Users can\ndefine dimension agnostic single layer or deep multi-layer\nnetworks without hard coded architecture limits, with per layer\nconfiguration vectors for activation functions, derivatives,\ndropout behavior, and initialization strategies automatically\naligned to network depth through controlled replication or\ntruncation. Reproducible workflows can be executed through high\nlevel helpers for fit, run, and predict across binary\nclassification, multi-class classification, and regression\nmodes. Training pipelines support optional self organization,\nadaptive learning rate behavior, and structured ensemble\norchestration in which candidate models are evaluated under\nuser specified performance metrics and selectively promoted or\npruned to refine a primary ensemble, enabling controlled\nensemble evolution over successive runs. Ensemble evaluation\nincludes fused prediction strategies in which member outputs\nmay be combined through weighted averaging, arithmetic\naveraging, or voting mechanisms to generate consolidated\nmetrics for research level comparison and reproducible per-seed\nassessment. The framework supports multiple optimization\napproaches, including stochastic gradient descent, adaptive\nmoment estimation, and look ahead methods, alongside\nconfigurable regularization controls such as L1, L2, and mixed\npenalties with separate weight and bias update logic.\nEvaluation features provide threshold tuning, relevance\nscoring, receiver operating characteristic and precision recall\ncurve generation, area under curve computation, regression\nerror diagnostics, and report ready metric outputs. The package\nalso includes artifact path management, debug state utilities,\nstructured run level metadata persistence capturing seeds,\nconfiguration states, thresholds, metrics, ensemble\ntransitions, fused evaluation artifacts, and model identifiers,\nas well as reproducible scripts and vignettes documenting end\nto end experiments. Kingma and Ba (2015)\n<doi:10.48550/arXiv.1412.6980> \"Adam: A Method for Stochastic\nOptimization\". Hinton et al. (2012)\n<https://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf>\n\"Neural Networks for Machine Learning (RMSprop lecture notes)\".\nDuchi et al. (2011) <https://jmlr.org/papers/v12/duchi11a.html>\n\"Adaptive Subgradient Methods for Online Learning and\nStochastic Optimization\". Zeiler (2012)\n<doi:10.48550/arXiv.1212.5701> \"ADADELTA: An Adaptive Learning\nRate Method\". Zhang et al. (2019)\n<doi:10.48550/arXiv.1907.08610> \"Lookahead Optimizer: k steps\nforward, 1 step back\". You et al. (2019)\n<doi:10.48550/arXiv.1904.00962> \"Large Batch Optimization for\nDeep Learning: Training BERT in 76 minutes (LAMB)\". McMahan et\nal. (2013) <https://research.google.com/pubs/archive/41159.pdf>\n\"Ad Click Prediction: a View from the Trenches\n(FTRL-Proximal)\". Klambauer et al. (2017)\n<https://proceedings.neurips.cc/paper/6698-self-normalizing-neural-networks.pdf>\n\"Self-Normalizing Neural Networks (SELU)\". Maas et al. (2013)\n<https://ai.stanford.edu/~amaas/papers/relu_hybrid_icml2013_final.pdf>\n\"Rectifier Nonlinearities Improve Neural Network Acoustic\nModels (Leaky ReLU / rectifiers)\".",
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  "URL": "https://github.com/MatHatter/DDESONN",
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  "Repository": "https://mathatter.r-universe.dev",
  "Date/Publication": "2026-03-09 05:13:33 UTC",
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  ],
  "_stars": 0,
  "_contributors": [
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      "user": "mathatter",
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      "uuid": 7362338
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  ],
  "_userbio": {
    "uuid": 7362338,
    "type": "user",
    "name": "MatHatter",
    "description": "DDESONN is meant for people to enjoy and love as it comes from a stance of passion. Super dedicated deveoper."
  },
  "_downloads": {
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    "source": "https://cranlogs.r-pkg.org/downloads/total/last-month/DDESONN"
  },
  "_devurl": "https://github.com/mathatter/ddesonn",
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  "_rbuild": "4.6.0",
  "_assets": [
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    "extra/citation.html",
    "extra/citation.json",
    "extra/citation.txt",
    "extra/contents.json",
    "extra/DDESONN.html",
    "extra/readme.html",
    "extra/readme.md",
    "manual.pdf"
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  "_homeurl": "https://github.com/mathatter/ddesonn",
  "_realowner": "mathatter",
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      "date": "2026-03-03"
    },
    {
      "version": "7.1.11",
      "date": "2026-03-11"
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  ],
  "_exports": [
    "arctangent",
    "arctangent_derivative",
    "bent_identity",
    "bent_identity_derivative",
    "bent_relu",
    "bent_relu_derivative",
    "bent_sigmoid",
    "bent_sigmoid_derivative",
    "bent_swish",
    "bent_swish_derivative",
    "binary_activation",
    "binary_activation_derivative",
    "custom_activation",
    "custom_activation_derivative",
    "custom_bent_piecewise",
    "custom_bent_piecewise_derivative",
    "custom_binary_activation",
    "custom_binary_activation_derivative",
    "ddesonn_activation_defaults",
    "DDESONN_activation_defaults",
    "ddesonn_artifacts_root",
    "DDESONN_artifacts_root",
    "ddesonn_debug_state",
    "ddesonn_dropout_defaults",
    "DDESONN_dropout_defaults",
    "ddesonn_fit",
    "DDESONN_fit",
    "ddesonn_model",
    "DDESONN_model",
    "ddesonn_optimizer_options",
    "DDESONN_optimizer_options",
    "ddesonn_plots_dir",
    "DDESONN_plots_dir",
    "ddesonn_predict",
    "DDESONN_predict",
    "ddesonn_run",
    "DDESONN_run",
    "ddesonn_training_defaults",
    "DDESONN_training_defaults",
    "elu",
    "elu_derivative",
    "gaussian",
    "gaussian_derivative",
    "gelu",
    "gelu_derivative",
    "hard_sigmoid",
    "hard_sigmoid_derivative",
    "identity",
    "identity_derivative",
    "inverse_linear_unit",
    "inverse_linear_unit_derivative",
    "isrlu",
    "isrlu_derivative",
    "leaky_bent",
    "leaky_bent_derivative",
    "leaky_relu",
    "leaky_relu_derivative",
    "leaky_selu",
    "leaky_selu_derivative",
    "maxout",
    "maxout_derivative",
    "mish",
    "mish_derivative",
    "parametric_bent_relu",
    "parametric_bent_relu_derivative",
    "prelu",
    "prelu_derivative",
    "relu",
    "relu_derivative",
    "selu",
    "selu_derivative",
    "sigmoid",
    "sigmoid_binary",
    "sigmoid_binary_derivative",
    "sigmoid_derivative",
    "sigmoid_sharp",
    "sigmoid_sharp_derivative",
    "sinusoid",
    "sinusoid_derivative",
    "softmax",
    "softmax_derivative",
    "softplus",
    "softplus_derivative",
    "swish",
    "swish_derivative",
    "tanh",
    "tanh_derivative",
    "tanh_relu_hybrid",
    "tanh_relu_hybrid_derivative"
  ],
  "_help": [
    {
      "page": "ddesonn_activation_defaults",
      "title": "Legacy alias for 'ddesonn_activation_defaults()'",
      "topics": [
        "DDESONN_activation_defaults",
        "ddesonn_activation_defaults"
      ]
    },
    {
      "page": "ddesonn_activation_derivatives",
      "title": "Activation derivatives (DDESONN)",
      "topics": [
        "arctangent_derivative",
        "bent_identity_derivative",
        "bent_relu_derivative",
        "bent_sigmoid_derivative",
        "bent_swish_derivative",
        "binary_activation_derivative",
        "custom_activation_derivative",
        "custom_bent_piecewise_derivative",
        "custom_binary_activation_derivative",
        "ddesonn_activation_derivatives",
        "elu_derivative",
        "gaussian_derivative",
        "gelu_derivative",
        "hard_sigmoid_derivative",
        "identity_derivative",
        "inverse_linear_unit_derivative",
        "isrlu_derivative",
        "leaky_bent_derivative",
        "leaky_relu_derivative",
        "leaky_selu_derivative",
        "maxout_derivative",
        "mish_derivative",
        "parametric_bent_relu_derivative",
        "prelu_derivative",
        "relu_derivative",
        "selu_derivative",
        "sigmoid_binary_derivative",
        "sigmoid_derivative",
        "sigmoid_sharp_derivative",
        "sinusoid_derivative",
        "softmax_derivative",
        "softplus_derivative",
        "swish_derivative",
        "tanh_derivative",
        "tanh_relu_hybrid_derivative"
      ]
    },
    {
      "page": "ddesonn_activations",
      "title": "Activation functions (DDESONN)",
      "topics": [
        "arctangent",
        "bent_identity",
        "bent_relu",
        "bent_sigmoid",
        "bent_swish",
        "binary_activation",
        "custom_activation",
        "custom_bent_piecewise",
        "custom_binary_activation",
        "ddesonn_activations",
        "elu",
        "gaussian",
        "gelu",
        "hard_sigmoid",
        "identity",
        "inverse_linear_unit",
        "isrlu",
        "leaky_bent",
        "leaky_relu",
        "leaky_selu",
        "maxout",
        "mish",
        "parametric_bent_relu",
        "prelu",
        "relu",
        "selu",
        "sigmoid",
        "sigmoid_binary",
        "sigmoid_sharp",
        "sinusoid",
        "softmax",
        "softplus",
        "swish",
        "tanh",
        "tanh_relu_hybrid"
      ]
    },
    {
      "page": "ddesonn_artifacts_root",
      "title": "Legacy alias for 'ddesonn_artifacts_root()'",
      "topics": [
        "DDESONN_artifacts_root",
        "ddesonn_artifacts_root"
      ]
    },
    {
      "page": "ddesonn_debug_state",
      "title": "Inspect internal DDESONN debug state",
      "topics": [
        "ddesonn_debug_state"
      ]
    },
    {
      "page": "ddesonn_dropout_defaults",
      "title": "Legacy alias for 'ddesonn_dropout_defaults()'",
      "topics": [
        "DDESONN_dropout_defaults",
        "ddesonn_dropout_defaults"
      ]
    },
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