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:
Downloads:
Reverse dependencies:
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