Changes in version 0.8.0                        

New features

  - groups and group_penalty arguments: every level of one or more
    grouping factors gets its own learned intercept on the linear
    predictor, trained jointly with the network (fixed-effect style
    cluster or period intercepts, as in panel data), with one ridge
    penalty overall or one per grouping factor. New group_effects()
    extracts them.
  - offset argument, in densemlp() and predict(): a fixed term added to
    the linear predictor (a vector, or a one-column-per-class matrix for
    multiclass).
  - task = "count": Poisson log-likelihood with a log link, for
    non-negative counts.
  - task = "binary" accepts numeric proportions in [0, 1] (a fractional
    or quasi-binomial outcome).
  - validation accepts a vector of (at least two) row indices to hold
    out, for grouped or time-ordered validation splits.
  - hidden_units = integer(0) fits a linear model (no hidden layers),
    useful as a GLM baseline sharing the same offset and group terms.

Bug fixes

  - The binary validation (and reported training) loss now uses the full
    Bernoulli cross-entropy. It previously dropped the (1 - y) log(1 -
    p) term, so early stopping for task = "binary" tracked a criterion
    that rewards over-predicting the positive class. Gradients were
    already correct.

                 Changes in version 0.7.1 (2026-09-01)                  

  - Add src/Makevars / src/Makevars.win linking the numerical kernels
    against R's BLAS/LAPACK ($(LAPACK_LIBS) $(BLAS_LIBS) $(FLIBS)).
    Without the explicit link line the Armadillo calls (dgemm_, dgemv_,
    ddot_, dsyrk_, ...) were left undefined at link time on the CRAN
    Windows builder. No user-visible changes.

                        Changes in version 0.7.0                        

Backend rewrite

  - The model backend no longer uses torch. Forward propagation,
    backpropagation and Adam optimization are now implemented natively
    in C++ via RcppArmadillo, with hand-derived closed-form gradients.
    The torch (and transitive libtorch) dependency is gone, so the
    package installs and trains without downloading a deep-learning
    runtime. ggplot2 is likewise no longer a dependency; plotting uses
    base graphics.

New capabilities

  - task = "survival": train against a survival::Surv(time, event)
    outcome with either loss = "cox" (batch-wise Breslow-tie Cox partial
    likelihood, the default) or loss = "brier" (discrete-time hazard
    head trained on the IPCW integrated Brier score). predict(type =
    "survival") returns the survival-probability curve for Brier-loss
    models; densemlp_metrics() reports Harrell's concordance index;
    densemlp_integrated_brier_score() evaluates a Brier-loss model's IBS
    on new data.
  - Formula interface: densemlp(formula = y ~ ., data = df) (including
    survival::Surv(time, status) ~ .) as an alternative to x/y. It must
    be passed by name, since x/y keep the first two positional slots.
  - residual, gated, learned cross-feature interaction, exponential
    moving-average weights (ema_decay), learning-rate schedules
    (lr_schedule), internal bootstrap ensembles (ensemble, ncores) and a
    tune_densemlp() grid search.

Argument changes

  - New batch_norm (default TRUE): set to FALSE for Linear -> ReLU
    hidden blocks with no normalization and no BN affine parameters.
  - New input_projection: an optional bare linear layer mapping the
    encoded predictors to a chosen dimension before the first hidden
    block. Cannot be combined with interaction.
  - task now accepts "binary" and "multiclass" explicitly (in addition
    to "regression", "survival" and the default "auto").
  - Removed the torch-era arguments that no longer apply: activation,
    optimizer, weight_decay and input_projection's old list form. The
    fitted object's class is now "densemlp" (was "densemlp_fit"), and
    its autoplot() method is replaced by plot() / plot_history().

Retained helpers

  - perm_importance() (model-agnostic permutation importance, now also
    covering survival) and plot_history() are kept, on the new backend.

                 Changes in version 0.6.0 (2026-08-22)                  

  - New cv_densemlp(): k-fold cross-validation with the same formula/x-y
    interface and task-aware defaults as densemlp() / tune_densemlp().
  - Standardized the maintainer's family-name casing to "El Badisy" in
    Authors@R.

                 Changes in version 0.5.0 (2026-08-08)                  

  - First CRAN release. No NEWS.md was kept prior to this version; see
    the git history for the earlier development log.