CRAN/E | cito

cito

Building and Training Neural Networks

Installation

About

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.

Citation cito citation info
citoverse.github.io/cito/
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Key Metrics

Version 1.1
R ≥ 3.5
Published 2024-03-18 11 days ago
Needs compilation? no
License GPL (≥ 3)
CRAN checks cito results
Language en-US

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Maintainer

Maintainer

Maximilian Pichler

maximilian.pichler@biologie.uni-regensburg.de

Authors

Christian Amesöder

aut

Maximilian Pichler

aut / cre

Florian Hartig

ctb

Armin Schenk

ctb

Material

README
NEWS
Reference manual
Package source

Vignettes

Introduction to cito
Training neural networks
Example: (Multi-) Species distribution models with cito
Advanced: Custom loss functions and prediction intervals

macOS

r-release

arm64

r-oldrel

arm64

r-release

x86_64

Windows

r-devel

x86_64

r-release

x86_64

r-oldrel

x86_64

Old Sources

cito archive

Depends

R ≥ 3.5

Imports

coro
checkmate
torch
gridExtra
parabar
abind
progress
cli
torchvision
tibble
lme4

Suggests

spelling
rmarkdown
testthat
plotly
ggraph
igraph
stats
ggplot2
knitr