Provides a collection of efficient functions for working with
  individual ages and corresponding intervals. These include functions for
  conversion from an age to an interval, aggregation of ages with associated
  counts in to intervals and the splitting of interval counts based on
  specified age distributions.

Implements the Agnostic Fay-Herriot model, an extension of 
    the traditional small area model. In place of normal sampling errors, the 
    sampling error distribution is estimated with a Gaussian process to 
    accommodate a broader class of distributions. This flexibility is most 
    useful in the presence of bounded, multi-modal, or heavily skewed sampling 
    errors.

The 'agghoo' procedure is an alternative to usual cross-validation.
    Instead of choosing the best model trained on V subsamples, it determines
    a winner model for each subsample, and then aggregates the V outputs.
    For the details, see "Aggregated hold-out" by Guillaume Maillard,
    Sylvain Arlot, Matthieu Lerasle (2021) <arXiv:1909.04890>
    published in Journal of Machine Learning Research 22(20):1--55.

The use of structured elicitation to inform decision making has grown dramatically in recent decades, however, judgements from multiple experts must be aggregated into a single estimate. Empirical evidence suggests that mathematical aggregation provides more reliable estimates than enforcing behavioural consensus on group estimates. 'aggreCAT' provides state-of-the-art mathematical aggregation methods for elicitation data including those defined in Hanea, A. et al. (2021) <doi:10.1371/journal.pone.0256919>. The package also provides functions to visualise and evaluate the performance of your aggregated estimates on validation data.

Convenience functions for aggregating a data frame or data table.
    Currently mean, sum and variance are supported. For Date variables, the recency
    and duration are supported. There is also support for dummy variables in
    predictive contexts. Code has been completely re-written in data.table for computational speed.