Package: densemlp
Title: Dense Neural Networks for Tabular Regression, Classification and
        Survival
Version: 0.7.1
Authors@R: 
    person("Imad", "El Badisy", email = "elbadisyimad@gmail.com", role = c("aut", "cre"))
Description: 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.
URL: https://CRAN.R-project.org/package=densemlp
BugReports: https://github.com/ielbadisy/densemlp/issues
License: MIT + file LICENSE
Encoding: UTF-8
RoxygenNote: 7.3.3
Imports: graphics, parallel, Rcpp, stats, utils
LinkingTo: Rcpp, RcppArmadillo
Suggests: knitr, rmarkdown, survival, testthat (>= 3.0.0)
Config/testthat/edition: 3
VignetteBuilder: knitr
NeedsCompilation: yes
Packaged: 2026-10-31 11:30:01 UTC; root
Author: Imad El Badisy [aut, cre]
Maintainer: Imad El Badisy <elbadisyimad@gmail.com>
Repository: https://cran.r-universe.dev
Date/Publication: 2026-09-01 08:30:19 UTC
RemoteUrl: https://github.com/cran/densemlp
RemoteRef: HEAD
RemoteSha: cf377be352c7da6659882c4ee5410a1754d3947b