kindling: A Higher-Level torch Interface for Generating, Training, and Tuning Neural Networks in R

Abstract

{kindling} is an R package that provides a higher-level interface to {torch}, R's native implementation of PyTorch, for defining, training, and tuning neural networks. It supports multilayer perceptrons and recurrent architectures (RNN, LSTM, GRU) while reducing the boilerplate typically required to write {torch} model definitions and training loops by hand. The package is organized around three levels of abstraction: code-generation functions that return inspectable, unevaluated {torch::nn\_module()} expressions; direct-training functions that fit a model from a formula and data frame; and {tidymodels}-registered model specifications that let neural networks be fit, tuned, and evaluated using the {tune}, {dials}, {recipes}, and {workflows} infrastructure that {tidymodels} users already rely on for other model types. This design lets analysts move from exploratory training to systematic hyperparameter search without leaving the {tidymodels} ecosystem, while retaining the ability to inspect or modify the generated model code rather than treating it as a black box.