Package: cito 1.1

Maximilian Pichler

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.

Authors:Christian Amesöder [aut], Maximilian Pichler [aut, cre], Florian Hartig [ctb], Armin Schenk [ctb]

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cito.pdf |cito.html✨
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NEWS

Peer review:

Bug tracker:https://github.com/citoverse/cito/issues

On CRAN:

machine-learningneural-network

12 exports 37 stars 3.20 score 50 dependencies 94 scripts 340 downloads

Last updated 2 months agofrom:49e596f777. Checks:OK: 1 WARNING: 6. Indexed: yes.

Exports:ALEanalyze_trainingconditionalEffectsconfig_lr_schedulerconfig_optimizerconfig_tuningcontinue_trainingdnnemultinomial_log_probPDPtune

Dependencies:abindbackportsbitbit64bootcallrcheckmateclicorocrayondescellipsisfansifilelockfsgluegridExtragtablehmsjpegjsonlitelatticelifecyclelme4magrittrMASSMatrixminqanlmenloptrparabarpillarpkgconfigpngprettyunitsprocessxprogresspsR6rappdirsRcppRcppEigenrlangsafetensorstibbletorchtorchvisionutf8vctrswithr