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(), rpart(), earth(),
    xgb.Booster.complete(), lgb.Booster(), catboost.Model(), cubist(), and
    ctree() models.

Download and read Brazilian Federal Highway Police ('PRF')
    open data on traffic accidents by person, by occurrence, and traffic
    violations
    <https://www.gov.br/prf/pt-br/acesso-a-informacao/dados-abertos/dados-abertos-da-prf>.
    Data are retrieved on demand as 'Parquet' files distributed via 'GitHub'
    releases and cached locally for reuse.

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.