Provides functions to construct finite-sample calibrated predictive 
    intervals for Bayesian models, following the approach in Barber et al. 
    (2021) <doi:10.1214/20-AOS1965>. These intervals are calculated efficiently
    using importance sampling for the leave-one-out residuals. By default,
    the intervals will also reflect the relative uncertainty in the Bayesian 
    model, using the locally-weighted conformal methods of Lei et al. (2018)
    <doi:10.1080/01621459.2017.1307116>.

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.

Methods and tools for performing multistep-ahead time series
    forecasting using conformal prediction methods including classical
    conformal prediction, adaptive conformal prediction, conformal PID
    (Proportional-Integral-Derivative) control, and autocorrelated
    multistep-ahead conformal prediction.
    The methods were described by Wang and Hyndman (2024) <doi:10.48550/arXiv.2410.13115>.

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 contains
    plot functions to visualize the output of the full and split conformal
    functions. To guarantee consistency, the package structure mimics the
    univariate package 'conformalInference' by Ryan Tibshirani.
    See Lei, G’sell, Rinaldo, Tibshirani, & Wasserman (2018) <doi:10.1080/01621459.2017.1307116>
    for full and split conformal prediction in regression, and Barber, Candès,
    Ramdas, & Tibshirani (2023) <doi:10.1214/23-AOS2276> for extensions beyond exchangeability.