conformalInference.multi-package: Conformal Inference Tools for Regression with Multivariate...

conformalInference.multi-packageR Documentation

Conformal Inference Tools for Regression with Multivariate Response

Description

It computes full conformal, split conformal and multi split conformal prediction regions when the response variable is multivariate (i.e. dimension is greater than one). Moreover, the package also contain plot functions to visualize the output of the full and split conformal functions.

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.multidim.split , i.e. a single split, and conformal.multidim.msplit , i.e. joining B splits. To guarantee consistency, the package structure mimics the univariate 'conformalInference' package of professor Ryan Tibshirani.

Author(s)

Maintainer: Paolo Vergottini paolo.vergottini@gmail.com

Authors:

  • Jacopo Diquigiovanni [thesis advisor]

  • Matteo Fontana matteo.fontana@ec.europa.eu [thesis advisor]

  • Aldo Solari [thesis advisor]

  • Simone Vantini [thesis advisor]

Other contributors:

  • Ryan Tibshirani [contributor]

References

  • "Distribution-Free Predictive Inference For Regression" by Lei et al. (2016) <arXiv:1604.04173>

  • "Conformal Prediction Bands for Multivariate Functional Data" by Diquigiovanni, Fontana, and Vantini (2021) <arXiv:2106.01792>

  • "The Importance of Being a Band: Finite-Sample Exact Distribution-Free Prediction Sets for Functional Data" by Diquigiovanni, Fontana, and Vantini (2021) <arXiv:2102.06746>

  • "Multi Split Conformal Prediction" by Solari, and Djordjilovic (2021) <arXiv:2103.00627>

See Also

Useful links:


conformalInference.multi documentation built on March 18, 2022, 5:45 p.m.