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: 2026-08-31 22:09:17 UTC; imad-el-badisy
Author: Imad El Badisy [aut, cre]
Maintainer: Imad El Badisy <elbadisyimad@gmail.com>
Repository: CRAN
Date/Publication: 2026-09-01 08:30:19 UTC
Built: R 4.6.1; x86_64-w64-mingw32; 2026-09-01 23:51:40 UTC; windows
Archs: x64
