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.

Bringing business and financial analysis to the 'tidyverse'. The 'tidyquant' 
    package provides a convenient wrapper to various 'xts', 'zoo', 'quantmod', 'TTR' 
    and 'PerformanceAnalytics' package 
    functions and returns the objects in the tidy 'tibble' format. The main 
    advantage is being able to use quantitative functions with the 'tidyverse'
    functions including 'purrr', 'dplyr', 'tidyr', 'ggplot2', 'lubridate', etc. See 
    the 'tidyquant' website for more information, documentation and examples.

Use 'SQL' 'SELECT' statements to query 'R' data
    frames.

Tools to help to create tidy data, where each column is a
    variable, each row is an observation, and each cell contains a single
    value.  'tidyr' contains tools for changing the shape (pivoting) and
    hierarchy (nesting and 'unnesting') of a dataset, turning deeply
    nested lists into rectangular data frames ('rectangling'), and
    extracting values out of string columns. It also includes tools for
    working with missing values (both implicit and explicit).