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.
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.
"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: