  Deep Neural Networks (DNN) have become a central method in ecology. Most
current deep learning (DL) applications rely on one of the major deep learning
frameworks, in particular Torch or TensorFlow, to build and train DNN. Using
these frameworks, however, requires substantially more experience and time than
typical regression functions in the R environment. Here, we present 'cito', a
user-friendly R package for DL that allows specifying DNNs in the familiar
formula syntax used by many R packages. To fit the models, 'cito' uses 'torch',
taking advantage of the numerically optimized torch library, including the
ability to switch between training models on the CPU or the graphics processing
unit (GPU) (which allows to efficiently train large DNN). Moreover, 'cito'
includes many user-friendly functions for model plotting and analysis,
including optional confidence intervals (CIs) based on bootstraps for
predictions and explainable AI (xAI) metrics for effect sizes and variable
importance with CIs and p-values. To showcase a typical analysis pipeline using
'cito', including its built-in xAI features to explore the trained DNN, we
build a species distribution model of the African elephant. We hope that by
providing a user-friendly R framework to specify, deploy and interpret DNN,
'cito' will make this interesting model class more accessible to ecological
data analysis. A stable version of 'cito' can be installed from the
comprehensive R archive network (CRAN).
