Parse XML documents from the Open Access subset of Europe PubMed Central <https://europepmc.org>
    including section paragraphs, tables, captions and references.

We provide a tidy grammar of population genetics, facilitating 
    the manipulation and analysis of data on biallelic single nucleotide
    polymorphisms (SNPs). 'tidypopgen' scales to very large genetic datasets
    by storing genotypes on disk, and performing operations on them in 
    chunks, without ever loading all data in memory. The full
    functionalities of the package are described in Carter et al. (2025)
    <doi:10.1101/2025.06.06.658325>.

Bayesian analysis used here to answer the question: "when
    looking at resampling results, are the differences between models
    'real'?" To answer this, a model can be created were the performance
    statistic is the resampling statistics (e.g. accuracy or RMSE). These
    values are explained by the model types. In doing this, we can get
    parameter estimates for each model's affect on performance and make
    statistical (and practical) comparisons between models. The methods
    included here are similar to Benavoli et al (2017)
    <https://jmlr.org/papers/v18/16-305.html>.

It parses a fitted 'R' model object, and returns a formula in
    'Tidy Eval' code that calculates the predictions.  It works with
    several databases back-ends because it leverages 'dplyr' and 'dbplyr'
    for the final 'SQL' translation of the algorithm. It currently
    supports lm(), glm(), randomForest(), ranger(), earth(),
    xgb.Booster.complete(), cubist(), and ctree() models.

Easily construct prompts and associated logic for interacting with 
  large language models (LLMs). 
  'tidyprompt' introduces the concept of prompt wraps, which are building
  blocks that you can use to quickly turn a simple prompt into a complex
  one. Prompt wraps do not just modify the prompt text, but also add
  extraction and validation functions that will be applied to the response
  of the LLM. This ensures that the user gets the desired output.
  'tidyprompt' can add various features to prompts and their evaluation
  by LLMs, such as structured output, automatic feedback, retries, reasoning
  modes, autonomous R function calling, and R code generation and evaluation.
  It is designed to be compatible with any LLM provider that offers
  chat completion.