# Landscape of R packages for eXplainable Artificial Intelligence

arXiv: https://arxiv.org/abs/2009.13248 · HTML: https://mlsanity.com/paper/2009.13248/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Sep 2020.

## Built on by (most cited first, 2 of 2)

- [A Time Series Approach to Explainability for Neural Nets with Applications to Risk-Management and Fraud Detection](https://mlsanity.com/paper/2212.02906/) (2022)
- [midr: Learning from Black-Box Models by Maximum Interpretation Decomposition](https://mlsanity.com/paper/2506.08338/) (2025)

## Abstract (arXiv)

The growing availability of data and computing power fuels the development of predictive models. In order to ensure the safe and effective functioning of such models, we need methods for exploration, debugging, and validation. New methods and tools for this purpose are being developed within the eXplainable Artificial Intelligence (XAI) subdomain of machine learning. In this work (1) we present the taxonomy of methods for model explanations, (2) we identify and compare 27 packages available in R to perform XAI analysis, (3) we present an example of an application of particular packages, (4) we acknowledge recent trends in XAI. The article is primarily devoted to the tools available in R, but since it is easy to integrate the Python code, we will also show examples for the most popular libraries from Python.