neuralnetwork 0.1.1

Maintenance release.

• Added optimizer convergence metadata to fitted models. L-BFGS fits now expose the convergence code and message returned by stats::optim(), and summary() prints that diagnostic.
• Added an explicit stratified cross-validation guard: when stratify = TRUE, each class must have at least k rows. This fails early with a clear message instead of producing a later fold-specific class mismatch.
• Added error_action = "continue" to nn_tune() so exploratory grids can record failed candidates and continue ranking the usable fits.
• Added explicit outcome-length checks for evaluation, permutation importance, and finite-difference Hessian helpers.
• Improved prediction errors for missing predictors and factor levels not seen during fitting.
• Added validation for classification target matrices and for missing or empty inputs to nn_class_ind().
• Added explicit validation for finite-difference step sizes, Hessian parameter limits, and confidence levels.
• Added stricter validation for optimizer hyperparameters, random seeds, integer loop counts, and callback return values.
• Clarified L-BFGS printed output so it reports function evaluations rather than epochs.
• Hardened the local CRAN check script so it parses R CMD check status lines and treats ERROR/WARNING results as failures.
• Added tests and documentation for the new diagnostics and cross-validation validation rule.

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 tasks.
• Added a worked-example vignette.