A tidy approach to analysis of biological sequences. All processing and data-storage functions are heavily optimized to allow the fastest and most efficient data storage.

Save the output of statistical tests in an organized file that can 
  be shared with others or used to report statistics in scientific papers.

Functions to generate stop-word lists in 110 languages, in a way consistent across all the languages supported. The generated lists are based on the morphological tagset from the Universal Dependencies.

Calculation of string distance following the tidy
    data principles. Built on top of the 'stringdist' package.

Provides a tidy set of functions for summarising data, 
    including descriptive statistics, frequency tables with normality testing, 
    and group-wise significance testing. Designed for fast, readable, 
    and easy exploration of both numeric and categorical data.

Streamlines the analysis of clinical data by automatically selecting appropriate statistical descriptions and inference methods based on variable types. For method details see Motulsky H J (2016) <https://www.graphpad.com/guides/prism/10/statistics/index.htm> and d'Agostino R B (1971) <doi:10.1093/biomet/58.2.341>.

A synthetic control offers a way of evaluating the effect of an intervention in comparative case studies. The package makes a number of improvements when implementing the method in R. These improvements allow users to inspect, visualize, and tune the synthetic control more easily. A key benefit of a tidy implementation is that the entire preparation process for building the synthetic control can be accomplished in a single pipe.

A tidy interface to 'data.table',
  giving users the speed of 'data.table' while using tidyverse-like syntax.

Extension of the 'tidyverse' for 'SpatRaster' and
    'SpatVector' objects of the 'terra' package. It includes also new
    'geom_' functions that provide a convenient way of visualizing 'terra'
    objects with 'ggplot2'.

Using tidy data principles can make many text mining tasks
    easier, more effective, and consistent with tools already in wide use.
    Much of the infrastructure needed for text mining with tidy data
    frames already exists in packages like 'dplyr', 'broom', 'tidyr', and
    'ggplot2'. In this package, we provide functions and supporting data
    sets to allow conversion of text to and from tidy formats, and to
    switch seamlessly between tidy tools and existing text mining
    packages.