NEWS.md

In densemlp: Dense Neural Networks for Tabular Regression, Classification and Survival

densemlp 0.9.0

New features

• weights argument in densemlp(): non-negative case weights, one per row, so the network minimizes a weighted empirical risk (importance weights under covariate shift, survey or inverse-probability weights). Only relative weights matter. Weights enter the loss and gradient of every task (a weighted Breslow partial likelihood for Cox, a weighted IPCW Brier score with a weighted censoring Kaplan-Meier and time grid), the batch-normalization statistics, the input and outcome standardization, and the validation loss used for early stopping. Unweighted fits are unchanged.
• weights argument in densemlp_metrics(): weighted versions of every metric (weighted AUC and C-index count each pair as the product of its weights).
• weights argument in cv_densemlp() (folds are fitted with their training weights and scored with weighted metrics, i.e. importance-weighted cross-validation) and tune_densemlp() (candidates ranked by the weighted validation loss).

densemlp 0.8.1

Bug fixes

• ncores > 1 (ensembles, cv_densemlp(), tune_densemlp()) could hang when R uses a threaded BLAS such as MKL or OpenBLAS: forked workers deadlocked in the BLAS/OpenMP thread pool inherited from the parent. Each worker now runs its BLAS on one thread (new import: RhpcBLASctl).

densemlp 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.

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

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.

densemlp 0.6.0

• 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.

densemlp 0.5.0

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

densemlp documentation built on Nov. 3, 2026, 1:09 a.m.