cito
Building and Training Neural Networks
The 'cito' package provides a user-friendly interface for training and interpreting deep neural networks (DNN). 'cito' simplifies the fitting of DNNs by supporting the familiar formula syntax, hyperparameter tuning under cross-validation, and helps to detect and handle convergence problems. DNNs can be trained on CPU, GPU and MacOS GPUs. In addition, 'cito' has many downstream functionalities such as various explainable AI (xAI) metrics (e.g. variable importance, partial dependence plots, accumulated local effect plots, and effect estimates) to interpret trained DNNs. 'cito' optionally provides confidence intervals (and p-values) for all xAI metrics and predictions. At the same time, 'cito' is computationally efficient because it is based on the deep learning framework 'torch'. The 'torch' package is native to R, so no Python installation or other API is required for this package.
- Version1.1
- R versionunknown
- LicenseGPL (≥ 3)
- Needs compilation?No
- Languageen-US
- cito citation info
- Last release03/18/2024
Documentation
Team
Maximilian Pichler
Florian Hartig
Show author detailsRolesContributorChristian Amesöder
Show author detailsRolesAuthorArmin Schenk
Show author detailsRolesContributor
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- Imports11 packages
- Suggests8 packages
- Reverse Imports1 package