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neuralnetwork: Fast Compact Multilayer Perceptrons

A small multilayer perceptron implementation for 'R'. It supports regression and classification, multiple hidden layers, mini-batch training, Adam, SGD, momentum, Nesterov, RPROP, GRPROP and L-BFGS optimizers, dropout, L2 regularization, early stopping, convergence thresholds, gradient clipping, sample and class weights, callback hooks, target scaling and robust Huber loss for regression, 'Rcpp' forward-pass kernels, formula interfaces, model evaluation with balanced classification metrics, cross-validation, compact tuning, permutation importance, model persistence helpers, and 'S3' prediction methods. Methods follow Rumelhart, Hinton and Williams (1986) <doi:10.1038/323533a0>, with optimizers including Riedmiller and Braun (1993) <doi:10.1109/ICNN.1993.298623>, Nocedal (1980) <doi:10.1090/S0025-5718-1980-0572855-7>, and Kingma and Ba (2014) <doi:10.48550/arXiv.1412.6980>.

Version: 0.1.0
Depends: R (≥ 4.1.0)
Imports: Rcpp
LinkingTo: Rcpp
Suggests: knitr, rmarkdown
Published: 2026-06-20
DOI: 10.32614/CRAN.package.neuralnetwork
Author: Feng Ji [aut, cre]
Maintainer: Feng Ji <f.ji at utoronto.ca>
License: MIT + file LICENSE
NeedsCompilation: yes
Language: en-US
Materials: README, NEWS
CRAN checks: neuralnetwork results

Documentation:

Reference manual: neuralnetwork.html , neuralnetwork.pdf
Vignettes: A practical workflow with neuralnetwork (source, R code)

Downloads:

Package source: neuralnetwork_0.1.0.tar.gz
Windows binaries: r-devel: neuralnetwork_0.1.0.zip, r-release: neuralnetwork_0.1.0.zip, r-oldrel: neuralnetwork_0.1.0.zip
macOS binaries: r-release (arm64): neuralnetwork_0.1.0.tgz, r-oldrel (arm64): neuralnetwork_0.1.0.tgz, r-release (x86_64): neuralnetwork_0.1.0.tgz, r-oldrel (x86_64): neuralnetwork_0.1.0.tgz

Linking:

Please use the canonical form https://CRAN.R-project.org/package=neuralnetwork to link to this page.

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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