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

Dense feed-forward neural networks (multilayer perceptrons) for tabular regression, classification and survival analysis, with a formula or x/y interface. Supports residual and gated hidden blocks, batch normalization, per-layer dropout, learned cross-feature interactions, exponential moving-average weights, learning-rate schedules, internal bootstrap ensembles, case weights (for example importance weights under covariate shift) and Adam optimization. Survival outcomes are trained with either a batch-wise Breslow-tie Cox partial likelihood or a discrete-time inverse-probability-of-censoring-weighted integrated Brier score. The numerical kernels are implemented natively in C++ via 'RcppArmadillo', with no external deep learning framework dependency (no 'torch' / 'libtorch'). Companion helpers provide k-fold cross-validation, hyperparameter search and task-aware evaluation metrics.

install.packages("densemlp")

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