Similar packages
All packages discussed here are built on top of torch, R’s native implementation of PyTorch. The torch package provides low-level tensor operations and neural network building blocks, but requires substantial boilerplate code for training. Higher-level packages like kindling, brulee, cito, and luz simplify this process while offering different features and design philosophies.
kindling distinguishes itself through its unique code generation approach, versatile neural architecture support (can be expanded more in the future), and three-level API design. While brulee focuses on production-ready statistical models, cito emphasizes explainability and statistical inference, and luz provides adaptable training loops. kindling is different but not mutually exclusive to them: it offers deep architectural control and bridges the gap between torch code and tidymodels workflows.
Package Comparison
| Feature | kindling | brulee | cito | luz |
|---|---|---|---|---|
| Primary Focus | Architectural versatility & flexibility, statistical modelling, and code generation | Production-ready statistical models | Statistical inference & interpretation | Training loop abstraction |
| Design Philosophy | Three-level API (code gen, training, ML framework (currently tidymodels) integration) | Batteries-included with sensible defaults | User-friendly with comprehensive xAI pipeline | High-level API reducing boilerplate |
| Architectures | Versatile — Feedforward Neural Networks (DNN/FFNN/MLP), Recurrent Neural Networks (RNN, LSTM, GRU), and more (in the future) | MLP, Linear/Logistic/Multinomial regression | Fully-connected networks, CNNs | Any torch nn_module |
| Code Generation | Yes (inspect & modify torch code) | No | No | No |
| tidymodels Integration | Full (parsnip models & tuning) | Full (official tidymodels package) | No (standalone package) | No (standalone package) |
| Formula Syntax | Yes | Yes | Yes | No (uses torch modules directly) |
| Layer-specific Activations | Yes | No | No | No (also uses torch modules directly) |
| GPU Support | Yes | Yes | Yes (CPU, GPU, MacOS) | Yes (automatic device placement) |
| Explainability/xAI | Garson’s & Olden’s algorithms, vip integration, and more in the future | Limited | Extensive (PDP, ALE, variable importance, etc.) | No |
| Statistical Inference | Not yet implemented | No | Yes (confidence intervals, p-values via bootstrap) | No |
| Custom Loss Functions | Yes | No | Yes | Yes |
| For whom? | Wanted versatile architectures (more in the future), fine-grained control, tidymodels users | Wants standard supervised learning, stable production models | Do ecological modeling, interpretable models, statistical inference | Wants custom architectures, users needing human-friendly training loop control |
Complementary Use
These packages aren’t mutually exclusive. You can use kindling and brulee for production MLPs, except kindling provides RNNs. The cito package is when you need oriented model interpretation. Then, the luz package is ideal when you want less verbose training loops. Despite the difference between the philosophies and main usage, all integrate with or build upon the torch ecosystem, allowing you to switch between them as your modeling needs evolve.
For instance, prototyping with kindling to explore different network architectures is much easier, as well as deploying its models for production, just like brulee, but cito is when stakeholders need detailed explanations of model predictions.