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neuralnetwork
View on CRAN: Click
here
Download and install neuralnetwork package within the R console
Install from CRAN:
install.packages("neuralnetwork")
Install from Github:
library("remotes")
install_github("cran/neuralnetwork") Install by package version:
library("remotes")
install_version("neuralnetwork", "0.1.0") Attach the package and use:
library("neuralnetwork")
Maintained by
Feng Ji
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[Scholar Profile | Author Map]
All associated links for this package
First Published: 2026-06-20
Latest Update: 2026-06-20
Description:
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>.
How to cite:
Feng Ji (2026). neuralnetwork: Fast Compact Multilayer Perceptrons. R package version 0.1.0, https://cran.r-project.org/web/packages/neuralnetwork. Accessed 14 Jul. 2026.
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