densemlp 0.7.1

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.

densemlp 0.7.0

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.

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.

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().

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

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.

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