# Automated Residual Plot Assessment With the R Package autovi and the Shiny Application autovi.web

arXiv: https://arxiv.org/abs/2606.24236 · HTML: https://mlsanity.com/paper/2606.24236/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 45 references that are in the map.
- First posted on arXiv 23 Jun 2026.

## Builds on (most cited first, 3 of 3)

- [TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems](https://mlsanity.com/paper/1603.04467/) (2016)
- [Global simulation envelopes for diagnostic plots in regression models](https://mlsanity.com/paper/2208.01811/) (2022)
- [Automated Assessment of Residual Plots with Computer Vision Models](https://mlsanity.com/paper/2411.01001/) (2024)

## Abstract (arXiv)

Visual assessment of residual plots is a common approach for diagnosing linear models, but it relies on manual evaluation, which does not scale well and can lead to inconsistent decisions across analysts. The lineup protocol, which embeds the observed plot among null plots, can reduce subjectivity but requires even more human effort. In today's data-driven world, such tasks are well suited for automation. We present a new R package that uses a computer vision model to automate the evaluation of residual plots. An accompanying Shiny application is provided for ease of use. Given a sample of residuals, the model predicts a visual signal strength (VSS) and offers supporting information to help analysts assess model fit.