  Most existing examples of full conformal predictive systems, split-conformal
predictive systems, and cross-conformal predictive systems impose severe
restrictions on the adaptation of predictive distributions to the test object
at hand. In this paper we develop split-conformal and cross-conformal
predictive systems that are fully adaptive. Our method consists in calibrating
existing predictive systems; the input predictive system is not supposed to
satisfy any properties of validity, whereas the output predictive system is
guaranteed to be calibrated in probability. It is interesting that the method
may also work without the IID assumption, standard in conformal prediction.
