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).

Automate the modelling of age-structured population data
  using survey data, grid population estimates and urban-rural extents.

We propose an age-dependent topic modelling (ATM) model,
    providing a low-rank representation of longitudinal records of
    hundreds of distinct diseases in large electronic health record data sets. The model
    assigns to each individual topic weights for several disease topics;
    each disease topic reflects a set of diseases that tend to co-occur as
    a function of age, quantified by age-dependent topic loadings for each
    disease. The model assumes that for each disease diagnosis, a topic is
    sampled based on the individual’s topic weights (which sum to 1 across
    topics, for a given individual), and a disease is sampled based on the
    individual’s age and the age-dependent topic loadings (which sum to 1
    across diseases, for a given topic at a given age). The model
    generalises the Latent Dirichlet Allocation (LDA) model by allowing
    topic loadings for each topic to vary with age.
    References: Jiang (2023) <doi:10.1038/s41588-023-01522-8>.

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.