conformalInference.multi-package {conformalInference.multi} | R Documentation |
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.
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.
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]
"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>
Useful links: