R's default conflict management system gives the most
    recently loaded package precedence. This can make it hard to detect
    conflicts, particularly when they arise because a package update
    creates ambiguity that did not previously exist. 'conflicted' takes a
    different approach, making every conflict an error and forcing you to
    choose which function to use.

Provides utilities for working with various 'Confluence' API 
    <https://docs.atlassian.com/ConfluenceServer/rest/latest/>, including a
    functionality to convert an R Markdown document to 'Confluence' format and
    upload it to 'Confluence' automatically.

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.

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>.