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cff-version: 1.2.0
message: 'To cite package "densemlp" in publications use:'
type: software
license: MIT
title: 'densemlp: Dense Neural Networks for Tabular Regression, Classification and
  Survival'
version: 0.7.1
doi: 10.32614/CRAN.package.densemlp
abstract: 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.
authors:
- family-names: El Badisy
  given-names: Imad
  email: elbadisyimad@gmail.com
repository: https://cran.r-universe.dev
repository-code: https://github.com/ielbadisy/densemlp
commit: cf377be352c7da6659882c4ee5410a1754d3947b
url: https://CRAN.R-project.org/package=densemlp
date-released: '2026-09-01'
contact:
- family-names: El Badisy
  given-names: Imad
  email: elbadisyimad@gmail.com