conformalInference.fd-package: Tools for conformal inference in regression in functional...

In paolo-vergo/conformalInference.fd: Tools for Conformal Inference for Regression in Multivariate Functional Setting

Tools for conformal inference in regression in functional setting

Description

A collection of tools for distribution-free inference for regression problems in functional setting using the theory of conformal prediction.

Details

Conformal inference is a framework for converting any pre-chosen estimator of the regression function into prediction regions with finite-sample validity, under essentially no assumptions on the data-generating process (aside from the the assumption of i.i.d. observations). The main functions in this package for computing such prediction regions are conformal.fun.split , i.e. a single split, and conformal.fun.msplit , i.e. joining B splits.

Author(s)

Maintainer: Paolo Vergottini [e-mail]

Authors:

• Jacopo Diquigiovanni

• Matteo Fontana [e-mail]

• Aldo Solari

• Simone Vantini

References

The function structure is taken from "Conformal Prediction Bands for Multivariate Functional Data" by Diquigiovanni, Fontana, Vantini (2021) and, also, from "The Importance of Being a Band: Finite-Sample Exact Distribution-Free Prediction Sets for Functional Data" by Diquigiovanni, Fontana, Vantini (2021).

paolo-vergo/conformalInference.fd documentation built on Oct. 14, 2023, 12:47 a.m.