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 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.

Version:	0.7.1
Imports:	graphics, parallel, Rcpp, stats, utils
LinkingTo:	Rcpp, RcppArmadillo
Suggests:	knitr, rmarkdown, survival, testthat (≥ 3.0.0)
Published:	2026-09-01
DOI:	10.32614/CRAN.package.densemlp
Author:	Imad El Badisy [aut, cre]
Maintainer:	Imad El Badisy <elbadisyimad at gmail.com>
BugReports:	https://github.com/ielbadisy/densemlp/issues
License:	MIT + file LICENSE
URL:	https://CRAN.R-project.org/package=densemlp
NeedsCompilation:	yes
Materials:	README, NEWS
CRAN checks:	densemlp results

Documentation:

Reference manual:	densemlp.html , densemlp.pdf
Vignettes:	Introduction to densemlp (source, R code)

Downloads:

Package source:	densemlp_0.7.1.tar.gz
Windows binaries:	r-devel: densemlp_0.7.1.zip, r-release: densemlp_0.7.1.zip, r-oldrel: densemlp_0.7.1.zip
macOS binaries:	r-release (arm64): densemlp_0.7.1.tgz, r-oldrel (arm64): densemlp_0.7.1.tgz, r-release (x86_64): densemlp_0.7.1.tgz, r-oldrel (x86_64): densemlp_0.7.1.tgz
Old sources:	densemlp archive

Reverse dependencies:

Reverse imports:

funcml, mimar

Linking:

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