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

Paolo Vergottini, Matteo Fontana, Jacopo Diquigiovanni, Aldo Solari, Simone Vantini

Research output: Contribution to journalArticle


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.
Original languageEnglish
Publication statusPublished - 2022


  • Computation (stat.CO)
  • FOS: Computer and information sciences
  • Methodology (stat.ME)

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