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Provides dense feed-forward neural network models for tabular regression and classification using 'torch'. The package supports modern extensions around dense neural network blocks, including dropout, batch normalization, residual connections, gated blocks, and optional input projection.
| Version: | 0.5.0 |
| Imports: | ggplot2, stats, torch |
| Suggests: | knitr, rmarkdown, testthat (≥ 3.0.0) |
| Published: | 2026-08-08 |
| 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://github.com/ielbadisy/densemlp |
| NeedsCompilation: | no |
| Materials: | README |
| CRAN checks: | densemlp results |
| Reference manual: | densemlp.html , densemlp.pdf |
| Vignettes: |
Getting Started with densemlp (source, R code) |
| Package source: | densemlp_0.5.0.tar.gz |
| Windows binaries: | r-devel: not available, r-release: densemlp_0.5.0.zip, r-oldrel: densemlp_0.5.0.zip |
| macOS binaries: | r-release (arm64): densemlp_0.5.0.tgz, r-oldrel (arm64): densemlp_0.5.0.tgz, r-release (x86_64): densemlp_0.5.0.tgz, r-oldrel (x86_64): densemlp_0.5.0.tgz |
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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.
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