neuralnetwork 0.1.4 (unreleased)

This update includes the unreleased 0.1.2 and 0.1.3 corrections. Users upgrading from 0.1.1 should recompute classification metrics and refit models affected by the weighted training, formula preprocessing or checkpoint-selection fixes.

Training and numerical stability

• Fixed weighted mini-batch gradients: weights no longer cancel in one-row batches. The normalization now matches the full weighted objective.
• Excluded zero-weight training rows from optimization, scalers and fitted formula bases. Fitted metadata records how many training rows were excluded.
• Finite gradient clipping now selects Adam automatically; explicit L-BFGS rejects the unsupported control instead of silently ignoring it.
• Shared L-BFGS loss/gradient forward passes and allocated optimizer state only when needed. These changes do not add runtime dependencies.
• Avoided cancellation in near-hard binary soft-target loss, and intermediate overflow/underflow in extreme multiclass loss and L2 gradients.
• Preserved RPROP direction-change detection for extremely small gradients by comparing signs rather than multiplying gradient magnitudes.
• Clarified errors for complex input and hidden-layer integers outside the supported range; rejected non-finite scaling before native kernels.
• Added deep-network gradient and weighted batch-average oracles, one-row weight checks, zero-weight invariance, cache instrumentation and allocation tests. Prior numerical and public-interface tests remain in the suite.
• Wrapped reference examples and help-page text for wider code fonts as well as the macOS manual font. No API defaults changed.

Evaluation, formulas and model selection

• Corrected multi-output R-squared centering and aligned training RMSE with evaluation RMSE. Constant truth returns an undefined R-squared explicitly.
• Preserved fitted formula bases and contrasts, applied response transformations during evaluation, and learned holdout bases from training rows only.
• Rejected mismatched response rows and missing named predictor columns; aligned named outcome matrices and accepted classification indicator/soft-target truth.
• Added sample-weighted evaluation and importance. Cross-validation now splits weights, stratifies encoded classes, retains undefined folds and reports score counts. All helpers use the fitted unpenalized data loss for metric = "loss".
• Tuning now compares candidates on one common holdout and ranks only finite scores. Candidate identifiers preserve the link to stored models.
• Used stable logits for classification objectives so saturated losses agree with gradients. Fixed the mini-batch L2 denominator, skipped zero-weight batches, rejected non-finite optimizer states and separated checkpoint saving from the patience improvement threshold.
• Removed fabricated intervals from singular Hessians. Parameter intervals are restricted to identifiable unweighted, unregularized no-hidden-layer linear or binary logistic L-BFGS fits, using total information and proper dispersion.
• Corrected sensitivities to use pre-scaling input units, fixed intercept-only parameter serialization and single-row permutations, and validated saved model structure on load. Unsupported L-BFGS epoch controls now fail explicitly.
• Added independent gradient, optimizer, regression, formula, selection and inference tests; tightened the release checker to reject any unapproved NOTE.
• Corrected plotting labels, included both loss series in the vertical range, drew single stored diagnostics as points, and stopped sensitivity plots from magnifying numerical noise in nearly constant derivatives.

Missed-class metrics

• Fixed inflated macro F1 and macro precision when a fitted class has no predicted observations. F1 now uses confusion-matrix counts directly, so wholly missed classes also contribute zero. Thanks to Graham Upton for the reproducible report.
• Macro precision, recall, and F1 now average over all fitted classes, using zero for a class-wise zero denominator. Binary metrics use the same rule; balanced accuracy still averages recall over classes present in the truth.
• Added regression tests for the reported matrix, 591 exhaustive binary and multiclass tables, and both prediction backends and model-selection paths.
• Made binary model-selection scores agree with prediction at a probability of exactly 0.5, and reject empty classification evaluations with a clear error.
• Documented the class averaging and zero-denominator rules with a worked confusion-matrix example.
• Clarified that printed fit metrics describe the selected checkpoint, not necessarily the last epoch.

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.