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.