Handles and formats author information in scientific writing
    in 'R Markdown' and 'Quarto'. 'plume' provides easy-to-use and
    flexible tools for inserting author data in 'YAML' as well as
    generating author and contribution lists (among others) as strings
    from tabular data.

Retrieves a 'pluscode' by inputting latitude and longitude. Includes additional functions to retrieve neighbouring 'pluscodes'.

Generates 'Plus Code' of geometric objects or data frames that contain them, giving the possibility to specify the precision of the area. The main feature of the package comes from the open-source code developed by 'Google Inc.' present in the repository <https://github.com/google/open-location-code/blob/main/java/src/main/java/com/google/openlocationcode/OpenLocationCode.java>. For details about 'Plus Code', visit <https://maps.google.com/pluscodes/> or <https://github.com/google/open-location-code>.

In ancient Roman mythology, 'Pluto' was the ruler of the underworld
  and presides over the afterlife. 'Pluto' was frequently conflated with 
  'Plutus', the god of wealth, because mineral wealth was found underground. 
  When plotting with R, you try once, twice, practice again and again, and finally 
  you get a pretty figure you want. It's a 'plot tour', a tour about repetition 
  and reward. Hope 'plutor' helps you on the tour!

A set of tools that solves a common set of problems: you need
    to break a big problem down into manageable pieces, operate on each
    piece and then put all the pieces back together.  For example, you
    might want to fit a model to each spatial location or time point in
    your study, summarise data by panels or collapse high-dimensional
    arrays to simpler summary statistics. The development of 'plyr' has
    been generously supported by 'Becton Dickinson'.

You can use this program for 3 sets of categorical data for propensity score matching.
            Assume that the data has 3 different categorical variables. You can use it to perform propensity matching of baseline indicator groupings. 
            The matching will make the differences in the baseline data smaller.
            This method was described by Alvaro Fuentes (2022) <doi:10.1080/00273171.2021.1925521>.