Pair of simple convenience functions to convert a vector of birth dates to age and age distributions. These functions may be helpful when related age and custom age distributions are desired given a vector of birth dates.

An R wrapper for 'agena.ai' <https://www.agena.ai> which provides users capabilities to work with 'agena.ai' using the R environment. Users can create Bayesian network models from scratch or import existing models in R and export to 'agena.ai' cloud or local API for calculations. Note: running calculations requires a valid 'agena.ai' API license (past the initial trial period of the local API).

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.

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.