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cito  

Building and Training Neural Networks
View on CRAN: Click here


Download and install cito package within the R console
Install from CRAN:
install.packages("cito")

Install from Github:
library("remotes")
install_github("cran/cito")

Install by package version:
library("remotes")
install_version("cito", "1.1")



Attach the package and use:
library("cito")
Maintained by
Maximilian Pichler
[Scholar Profile | Author Map]
First Published: 2022-08-11
Latest Update: 2023-03-13
Description:
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.
How to cite:
Maximilian Pichler (2022). cito: Building and Training Neural Networks. R package version 1.1, https://cran.r-project.org/web/packages/cito. Accessed 13 Apr. 2025.
Previous versions and publish date:
1.0.0 (2022-08-11 17:10), 1.0.1 (2023-03-13 14:00), 1.0.2 (2023-10-06 16:40)
Other packages that cited cito R package
View cito citation profile
Other R packages that cito depends, imports, suggests or enhances
Complete documentation for cito
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