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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).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).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.(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.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.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.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.batch_norm (default TRUE): set to
FALSE for Linear -> ReLU hidden blocks with
no normalization and no BN affine parameters.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").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.cv_densemlp(): k-fold cross-validation with the
same formula/x-y interface and task-aware defaults as
densemlp() / tune_densemlp().Authors@R.NEWS.md was kept prior to this
version; see the git history for the earlier development log.These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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