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neuralnetwork

neuralnetwork fits multilayer perceptrons for tabular data in R. It accepts formulas, data frames, matrices, and vectors; handles regression and classification; and includes tuning, cross-validation, metrics, feature importance, and model save/load helpers. It is meant for users who need more than nnet’s single-hidden-layer interface or neuralnet’s manual training style, but do not want to bring in a full deep-learning stack.

Install

install.packages("neuralnetwork")

To install the local source tarball:

install.packages("neuralnetwork_0.1.1.tar.gz", repos = NULL, type = "source")

Quick start

library(neuralnetwork)

fit <- nn_fit(
  Species ~ .,
  data = iris,
  hidden = "auto",
  optimizer = "auto",
  epochs = 20,
  validation_split = 0.2,
  seed = 1,
  verbose = FALSE
)

fit
predict(fit, iris[1:5, ], type = "class")
round(predict(fit, iris[1:5, ], type = "prob"), 3)

ev <- nn_evaluate(fit, iris)
ev

The printed model reports the architecture, optimizer, loss, backend, training length, final training score, and validation score when available. nn_evaluate() returns the metrics as a named vector and prints a compact confusion matrix for classification.

Regression

For regression, put a numeric response on the left side of the formula. Training can scale the target internally; predictions are returned on the original response scale.

fit_reg <- nn_fit(
  mpg ~ wt + hp + disp,
  data = mtcars,
  hidden = c(8, 4),
  optimizer = "adam",
  epochs = 40,
  batch_size = 8,
  learning_rate = 0.01,
  validation_split = 0.2,
  seed = 2,
  verbose = FALSE
)

predict(fit_reg, mtcars[1:5, ])
nn_evaluate(fit_reg, mtcars)

For regression problems with outliers, use Huber loss:

fit_huber <- nn_fit(
  mpg ~ wt + hp + disp,
  data = mtcars,
  hidden = c(8, 4),
  optimizer = "adam",
  loss = "huber",
  huber_delta = 1,
  epochs = 40,
  batch_size = 8,
  learning_rate = 0.01,
  seed = 3,
  verbose = FALSE
)

Choosing settings

Reasonable first choices:

• hidden = "auto" for a small architecture chosen from the task and input width.
• optimizer = "auto" for L-BFGS on small deterministic problems and Adam when using stochastic features such as dropout or callbacks.
• For L-BFGS fits, epochs is the optimizer iteration limit and printed training length is reported as function evaluations.
• validation_split = 0.2 for validation loss, early stopping, or validation-based tuning.
• metric = "balanced_accuracy" for imbalanced classification, metric = "f1" when the positive class is the focus, and metric = "mae" or metric = "rmse" for regression.
• loss = "huber" for regression data where outliers may dominate squared error.

Tuning and validation

tuned <- nn_tune(
  Species ~ .,
  data = iris,
  grid = list(
    hidden = list(4, c(6, 3)),
    learning_rate = c(0.01, 0.003)
  ),
  metric = "balanced_accuracy",
  epochs = 8,
  validation_split = 0.2,
  seed = 4,
  verbose = FALSE
)

tuned
tuned$best_model

For exploratory grids, error_action = "continue" keeps candidate failures in the results table while ranking the usable fits.

cv <- nn_cv(
  Species ~ .,
  data = iris,
  k = 3,
  metric = "f1",
  hidden = 4,
  epochs = 5,
  seed = 5,
  verbose = FALSE
)

cv

Feature importance

imp <- nn_permutation_importance(
  fit_reg,
  mtcars,
  metric = "mae",
  n_repeats = 3,
  seed = 6
)

imp

Function map

What’s included

• Formula, data frame, matrix, and vector inputs.
• Regression, binary classification, and multiclass classification.
• Multiple hidden layers, including hidden = 0 for no hidden layer.
• Adam, SGD, momentum, Nesterov, RPROP, GRPROP, and L-BFGS optimizers.
• Automatic hidden-layer sizing, activation choice, and optimizer choice.
• Portable Rcpp forward-pass kernels when available.
• Dropout, L2 regularization, gradient clipping, learning-rate decay, validation splits, early stopping, and callbacks.
• Sample weights and balanced class weights.
• Huber loss for robust regression.
• Accuracy, balanced accuracy, macro F1, log loss, RMSE, MAE, and R-squared.
• Tuning, repeated k-fold cross-validation, permutation importance, save/load helpers, and S3 predict(), print(), plot(), summary(), and coef().
• Training history plus optimizer convergence metadata for inspecting fitted models, including L-BFGS convergence codes from stats::optim().
• Compatibility helpers for common nnet and neuralnet tasks: nn_multinom(), nn_class_ind(), nn_which_is_max(), nn_compute(), nn_generalized_weights(), nn_gwplot(), nn_hessian(), and nn_confint().

Run vignette("neuralnetwork") for the longer worked example.

Reference help inside R: ?neuralnetwork, ?neuralnetwork-metrics, ?neuralnetwork-callbacks, and ?neuralnetwork-objects.

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.
Health stats visible at Monitor.