cito
Deep neural networks in R with ordinary formula syntax, plus the explainable-AI tools to interpret them.
Cited 9 times on Google Scholar (as of 11 October 2026)
cito fits and interprets deep neural networks in R. A network is
specified with the same formula syntax as lm or glm, hyperparameters can
be tuned by cross-validation in the same call, and the package helps to detect
and handle convergence problems.
Predictions alone are rarely enough in ecology, so cito also provides the tools to look inside a fitted network: variable importance, partial dependence and accumulated local effect plots, and effect estimates, each optionally with confidence intervals and p-values. It is built on the R package torch, needs no Python installation, and runs on CPUs, GPUs and Mac GPUs.
Authors: Christian Amesöder, Maximilian Pichler (maintainer). Contributors: Florian Hartig, Armin Schenk.
Paper describing cito
Widely cited work citing cito
The most-cited articles that cite cito or its paper, ranked by their own citation count in OpenAlex; the group's own papers are left out.
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