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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 |
| Reference manual: | densemlp.html , densemlp.pdf |
| Vignettes: |
Introduction to densemlp (source, R code) |
| 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 imports: | funcml, mimar |
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.