Dense Neural Networks for Tabular Regression, Classification and Survival R package densemlp version 0.7.1 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 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. Software Imports: graphics, parallel, Rcpp, stats, utils LinkingTo: Rcpp, RcppArmadillo Suggests: knitr, rmarkdown, survival, testthat (>= 3.0.0) Imad El Badisy <elbadisyimad@gmail.com> Comprehensive R Archive Network (CRAN) Imad El Badisy [aut, cre] MIT + file LICENSE (https://CRAN.R-project.org/package=densemlp/LICENSE) 2026-09-01 application/tgz https://CRAN.R-project.org/package=densemlp doi:10.32614/CRAN.package.densemlp