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).

An AI-powered assistant for the R console. Type natural language
    or incorrect R code directly in the console, and 'agenticr' routes it to an
    AI agent for processing. Focused on statistical analysis, data
    transformation, and visualization, <https://github.com/panlanfeng/AgenticR>.

Specification, review, and scaffolding helpers for AI agent systems.
    The package standardizes workflow, memory, knowledge, interface, proposal,
    and review artifacts so humans and coding assistants can infer, inspect,
    revise, and hand off task designs. It intentionally excludes communication
    layers, provider-specific model client code, and full runtime execution
    engines so that design artifacts and implementation transport remain
    cleanly separated.

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>.