  Building on top of a regression model, Conformal Prediction methods produce
distribution free prediction sets, requiring only i.i.d. data. While R packages
implementing such methods for the univariate response framework have been
developed, this is not the case with multivariate and functional responses.
conformalInference.multi and conformalInference.fd address this void, by
extending classical and more advanced conformal prediction methods like full
conformal, split conformal, jackknife+ and multi split conformal to deal with
the multivariate and functional case. The extreme flexibility of conformal
prediction, fully embraced by the structure of the package, which does not
require any specific regression model, enables users to pass in any regression
function as input while using basic regression models as reference. Finally,
the issue of visualisation is addressed by providing embedded plotting
functions to visualize prediction regions.
