# fmeffects: An R Package for Forward Marginal Effects

arXiv: https://arxiv.org/abs/2310.02008 · HTML: https://mlsanity.com/paper/2310.02008/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 28 references that are in the map.
- First posted on arXiv 3 Oct 2023.

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

- [ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R](https://mlsanity.com/paper/1508.04409/) (2015)
- [Sampling, Intervention, Prediction, Aggregation: A Generalized Framework for Model-Agnostic Interpretations](https://mlsanity.com/paper/1904.03959/) (2019)
- [Marginal Effects for Non-Linear Prediction Functions](https://mlsanity.com/paper/2201.08837/) (2022)

## Abstract (arXiv)

Forward marginal effects have recently been introduced as a versatile and effective model-agnostic interpretation method particularly suited for non-linear and non-parametric prediction models. They provide comprehensible model explanations of the form: if we change feature values by a pre-specified step size, what is the change in the predicted outcome? We present the R package fmeffects, the first software implementation of the theory surrounding forward marginal effects. The relevant theoretical background, package functionality and handling, as well as the software design and options for future extensions are discussed in this paper.