conformalInference.fd-package: Tools for Conformal Inference for Regression in Multivariate...

In conformalInference.fd: Tools for Conformal Inference for Regression in Multivariate Functional Setting

Tools for Conformal Inference for Regression in Multivariate Functional Setting

Description

It computes split conformal and multi split conformal prediction regions when the response has functional nature. Moreover, the package also contain a plot function to visualize the output of the split conformal.

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. To guarantee consistency, the package structure mimics the univariate 'conformalInference' package of professor Ryan Tibshirani.

Author(s)

Maintainer: Paolo Vergottini [e-mail]

Authors:

• Jacopo Diquigiovanni [thesis advisor]

• Matteo Fontana [e-mail] [thesis advisor]

• Aldo Solari [thesis advisor]

• Simone Vantini [thesis advisor]

Other contributors:

• Ryan Tibshirani [contributor]

References

• "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:

• https://github.com/ryantibs/conformal

• https://github.com/paolo-vergo/conformalInference.fd

conformalInference.fd documentation built on March 23, 2022, 5:09 p.m.