Provides a fully native R deep learning framework for
    constructing, training, evaluating, and inspecting Deep Dynamic Ensemble
    Self Organizing Neural Networks at research scale. The core engine is an
    object oriented R6 class-based implementation with explicit control over
    layer layout, dimensional flow, forward propagation, back propagation, and
    transparent optimizer state updates. The framework does not rely on external
    deep learning back ends, enabling direct inspection of model state,
    reproducible numerical behavior, and fine grained architectural control
    without requiring compiled dependencies or graphics processing unit specific
    run times. Users can define dimension agnostic single layer or deep
    multi-layer networks without hard coded architecture limits, with per layer
    configuration vectors for activation functions, derivatives, dropout
    behavior, and initialization strategies automatically aligned to network
    depth through controlled replication or truncation. Reproducible workflows
    can be executed through high level helpers for fit, run, and predict across
    binary classification, multi-class classification, and regression modes.
    Training pipelines support optional self organization, adaptive learning
    rate behavior, and structured ensemble orchestration in which candidate
    models are evaluated under user specified performance metrics and
    selectively promoted or pruned to refine a primary ensemble, enabling
    controlled ensemble evolution over successive runs. Ensemble evaluation
    includes fused prediction strategies in which member outputs may be
    combined through weighted averaging, arithmetic averaging, or voting
    mechanisms to generate consolidated metrics for research level comparison
    and reproducible per-seed assessment. The framework supports multiple
    optimization approaches, including stochastic gradient descent,
    adaptive moment estimation, and look ahead methods, alongside configurable
    regularization controls such as L1, L2, and mixed penalties with separate
    weight and bias update logic. Evaluation features provide threshold tuning,
    relevance scoring, receiver operating characteristic and precision recall
    curve generation, area under curve computation, regression error diagnostics,
    and report ready metric outputs. The package also includes artifact path
    management, debug state utilities, structured run level metadata persistence
    capturing seeds, configuration states, thresholds, metrics, ensemble
    transitions, fused evaluation artifacts, and model identifiers, as well as
    reproducible scripts and vignettes documenting end to end experiments.
    Kingma and Ba (2015) <doi:10.48550/arXiv.1412.6980> "Adam: A Method for Stochastic Optimization".
    Hinton et al. (2012) <https://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf> "Neural Networks for Machine Learning (RMSprop lecture notes)".
    Duchi et al. (2011) <https://jmlr.org/papers/v12/duchi11a.html> "Adaptive Subgradient Methods for Online Learning and Stochastic Optimization".
    Zeiler (2012) <doi:10.48550/arXiv.1212.5701> "ADADELTA: An Adaptive Learning Rate Method".
    Zhang et al. (2019) <doi:10.48550/arXiv.1907.08610> "Lookahead Optimizer: k steps forward, 1 step back".
    You et al. (2019) <doi:10.48550/arXiv.1904.00962> "Large Batch Optimization for Deep Learning: Training BERT in 76 minutes (LAMB)".
    McMahan et al. (2013) <https://research.google.com/pubs/archive/41159.pdf> "Ad Click Prediction: a View from the Trenches (FTRL-Proximal)".
    Klambauer et al. (2017) <https://proceedings.neurips.cc/paper/6698-self-normalizing-neural-networks.pdf> "Self-Normalizing Neural Networks (SELU)".
    Maas et al. (2013) <https://ai.stanford.edu/~amaas/papers/relu_hybrid_icml2013_final.pdf> "Rectifier Nonlinearities Improve Neural Network Acoustic Models (Leaky ReLU / rectifiers)".