High performance variant of apply() for a fixed set of functions.
  Considerable speedup of this implementation is a trade-off for universality: user defined
  functions cannot be used with this package. However, about 20 most currently employed
  functions are available for usage. They can be divided in three types:
  reducing functions (like mean(), sum() etc., giving a scalar when applied to a vector),
  mapping function (like normalise(), cumsum() etc., giving a vector of the same length
  as the input vector) and finally, vector reducing function (like diff() which produces
  result vector of a length different from the length of input vector).
  Optional or mandatory additional arguments required by some functions
  (e.g. norm type for norm()) can be
  passed as named arguments in '...'.

Some convenient functions to work with arrays.

Argument parsing for R scripts, with support for long and short
    Unix-style options including option clustering, positional arguments
    including those of variable length, and multiple usage patterns which may
    take different subsets of options.

'Apache' 'Arrow' <https://arrow.apache.org/> is a cross-language
    development platform for in-memory data. It specifies a standardized
    language-independent columnar memory format for flat and hierarchical data,
    organized for efficient analytic operations on modern hardware. This
    package provides an interface to the 'Arrow C++' library.

The 'ggarrow' package is a 'ggplot2' extension that plots
    a variety of different arrow segments with many options to customize. 
    The 'arrowheadr' package makes it easy to create custom arrowheads 
    and fins within the parameters that 'ggarrow' functions expect. It 
    has preset arrowheads and a collection of functions to create and 
    transform data for customizing arrows.

Adaptive Rejection Sampling, Original version.

A method for quantifying resilience after a stress event. A set of functions
    calculate the area of resilience that is created by the departure of baseline 
    'y' (i.e., robustness) and the time taken 'x' to return to baseline 
    (i.e., rapidity) after a stress event using the Cartesian coordinates of the 
    data. This package has the capability to calculate areas of resilience,
    growth, and cases in which resilience is not achieved 
    (e.g., diminished performance without return to baseline).