conformalInference.multi and conformalInference.fd: Twin Packages for Conformal Prediction

TL;DR

This paper introduces two R packages, conformalInference.multi and conformalInference.fd, which extend conformal prediction methods to multivariate and functional responses, offering flexible, model-agnostic tools with visualization features.

Contribution

The packages fill a gap by enabling conformal prediction for multivariate and functional data, supporting any regression model and including visualization tools.

Findings

Extended conformal methods to multivariate and functional responses.

Flexible, model-agnostic implementation in R packages.

Includes visualization functions for prediction regions.

Abstract

Building on top of a regression model, Conformal Prediction methods produce distribution free prediction sets, requiring only i.i.d. data. While R packages implementing such methods for the univariate response framework have been developed, this is not the case with multivariate and functional responses. conformalInference.multi and conformalInference.fd address this void, by extending classical and more advanced conformal prediction methods like full conformal, split conformal, jackknife+ and multi split conformal to deal with the multivariate and functional case. The extreme flexibility of conformal prediction, fully embraced by the structure of the package, which does not require any specific regression model, enables users to pass in any regression function as input while using basic regression models as reference. Finally, the issue of visualisation is addressed by providing embedded plotting functions to visualize prediction regions.