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).

Compute age-adjusted rates by direct and indirect methods and other epidemiological indicators in a tidy way, wrapping functions from the 'epitools' package.

Helper functions for processing 'REDCap' data in R. 'REDCap' is a 
    web-enabled application for building and managing surveys and databases 
    developed at Vanderbilt University.

Provides 'tidyverse' methods for wrangling
    and analyzing 'Earth Engine' <https://earthengine.google.com/> data. These methods help the user with filtering, 
    joining and summarising 'Earth Engine' image collections.

With the objective of including data from RSS feeds into your analysis, 
    'tidyRSS' parses RSS, Atom and JSON feeds and returns a tidy data frame.

Utility to convert text based summary of rule based models to a tidy dataframe (where each row represents a rule) with related metrics such as support, confidence and lift. Rule based models from these packages are supported: 'C5.0', 'rpart' and 'Cubist'.

Fit species distribution models (SDMs) using the 'tidymodels' framework, 
  which provides a standardised interface to define models and process their 
  outputs. 'tidysdm' expands 'tidymodels' by providing methods for spatial objects,
  models and metrics specific to SDMs,
  as well as a number of specialised functions to process occurrences
  for contemporary and palaeo datasets. The full 
  functionalities of the package are described
  in Leonardi et al. (2023) <doi:10.1101/2023.07.24.550358>.