Implement download buttons in HTML output from 'rmarkdown' without the need for 'runtime:shiny'.

Uses species occupancy at coarse grain sizes to predict species occupancy at fine grain sizes. Ten models are provided to fit and extrapolate the occupancy-area relationship, as well as methods for preparing atlas data for modelling. See Marsh et. al. (2018) <doi:10.18637/jss.v086.c03>.

Toggles the test and production versions of a large
  data analysis project.

Provides a set of functions for inferring, visualizing, and analyzing B cell phylogenetic trees.
    Provides methods to 1) reconstruct unmutated ancestral sequences,
    2) build B cell phylogenetic trees using multiple methods,
    3) visualize trees with metadata at the tips,
    4) reconstruct intermediate sequences,
    5) detect biased ancestor-descendant relationships among metadata types
    Workflow examples available at documentation site (see URL).
    Citations:
    Hoehn et al (2022) <doi:10.1371/journal.pcbi.1009885>,
    Hoehn et al (2021) <doi:10.1101/2021.01.06.425648>.

Access diverse 'ggplot2'-compatible color palettes for
    simplified data visualization.

A Scannerless GLR parser/parser generator.  Note that GLR standing for "generalized LR", where L stands for "left-to-right" and
   R stands for "rightmost (derivation)".  For more information see <https://en.wikipedia.org/wiki/GLR_parser>. This parser is based on the Tomita
   (1987) algorithm. (Paper can be found at <https://aclanthology.org/P84-1073.pdf>).
   The original 'dparser' package documentation can be found at <https://dparser.sourceforge.net/>.  This allows you to add mini-languages to R (like
   rxode2's ODE mini-language Wang, Hallow, and James 2015 <DOI:10.1002/psp4.12052>) or to parse other languages like 'NONMEM' to automatically translate
   them to R code.   To use this in your code, add a LinkingTo dparser in your DESCRIPTION file and instead of using #include <dparse.h> use
   #include <dparser.h>.  This also provides a R-based port of the make_dparser <https://dparser.sourceforge.net/d/make_dparser.cat> command called
   mkdparser().  Additionally you can parse an arbitrary grammar within R using the dparse() function, which works on most OSes and is mainly for grammar
   testing.  The fastest parsing, of course, occurs at the C level, and is suggested.