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