Tools are provided to expand vectors of short URLs into long 'URLs'. 
    No 'API' services are used, which may mean that this operates more slowly than 
    'API' services do (since they usually cache results of expansions that every 
    user of the service requests). You can setup your own caching layer with the 
    'memoise' package if you wish to have a speedup during single sessions or add 
    larger dependencies, such as 'Redis', to gain a longer-term performance boost 
    at the expense of added complexity.

Efficient approximate leave-one-out cross-validation (LOO)
    for Bayesian models fit using Markov chain Monte Carlo, as described
    in Vehtari, Gelman, and Gabry (2017) <doi:10.1007/s11222-016-9696-4>.
    The approximation uses Pareto smoothed importance sampling (PSIS), a
    new procedure for regularizing importance weights.  As a byproduct of
    the calculations, we also obtain approximate standard errors for
    estimated predictive errors and for the comparison of predictive
    errors between models. The package also provides methods for using
    stacking and other model weighting techniques to average Bayesian
    predictive distributions.

Outlier detection using leave-one-out kernel density estimates and
    extreme value theory. The bandwidth for kernel density estimates is computed
    using persistent homology, a technique in topological data analysis. Using
    peak-over-threshold method, a generalized Pareto distribution is fitted to
    the log of leave-one-out kde values to identify outliers.

Simple functions to lookup items in key-value pairs. See
    Mehta (2021) <doi:10.1007/978-1-4842-6613-7_6>.

Fits look-up tables by filling entries with the mean or median values of observations
            fall in partitions of the feature space. Partitions can be determined by user of the
            package using input argument feature.boundaries, and dimensions of the feature space
            can be any combination of continuous and categorical features provided by the data set.
            A Predict function directly fetches corresponding entry value, and a default value is
            defined as the mean or median of all available observations.
            The table and other components are represented using the S4 class lookupTable.