Changes in version 0.1.0 (2026-06-20)                  

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.