Implementation of transductive conformal prediction (see Vovk, 2013, <doi:10.1007/978-3-642-41142-7_36>) and inductive conformal prediction (see Balasubramanian et al., 2014, ISBN:9780124017153) for classification problems.

It computes full conformal, 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.
    To guarantee consistency, the package structure mimics the univariate 
    'conformalInference' package of professor Ryan Tibshirani.
    The main references for the code are: 
    Diquigiovanni, Fontana, and Vantini (2021) <arXiv:2102.06746>, 
    Diquigiovanni, Fontana, and Vantini (2021) <arXiv:2106.01792>,
    Solari, and Djordjilovic (2021) <arXiv:2103.00627>.

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. 
    To guarantee consistency, the package structure mimics the univariate 'conformalInference'
    package of professor Ryan Tibshirani.
    The main references for the code are: 
    Lei et al. (2016) <arXiv:1604.04173>,
    Diquigiovanni, Fontana, and Vantini (2021) <arXiv:2102.06746>, 
    Diquigiovanni, Fontana, and Vantini (2021) <arXiv:2106.01792>,
    Solari, and Djordjilovic (2021) <arXiv:2103.00627>.

Computes marginal conformal p-values using conformal prediction in binary classification tasks. Conformal prediction is a framework that augments machine learning algorithms with a measure of uncertainty, in the form of prediction regions that attain a user-specified level of confidence. This package specifically focuses on providing conformal p-values that can be used to assess the confidence of the classification predictions. For more details, see Tyagi and Guo (2023) <https://proceedings.mlr.press/v204/tyagi23a.html>.