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neuralnetwork 0.1.0

First CRAN-oriented release.

• Added nn_fit() for compact multilayer perceptrons with formula, data frame, matrix, and vector inputs.
• Added regression, binary classification, and multiclass classification.
• Added Adam, SGD, momentum, Nesterov, RPROP, GRPROP, and L-BFGS optimizers.
• Added automatic hidden-layer sizing, optimizer selection, and activation selection.
• Added optional portable Rcpp forward-pass kernels.
• Added dropout, L2 regularization, gradient clipping, learning-rate decay, validation splits, early stopping, and callback hooks.
• Added sample weights and balanced class weights.
• Added robust Huber loss for regression.
• Added task-aware evaluation metrics, tuning, repeated k-fold cross-validation, and permutation importance.
• Added save/load helpers and S3 methods for prediction, printing, plotting, summaries, and coefficients.
• Added compatibility helpers for common nnet and neuralnet workflows.
• Added a practical workflow vignette.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.