Circular / ring buffers in R and C.  There are a couple
    of different buffers here with different implementations that
    represent different trade-offs.

Loading calls data from 'Ringostat API'. 
    See <https://help.ringostat.com/knowledge-base/article/integration-with-ringostat-via-api>.

C++ classes to embed R in C++ (and C) applications
 A C++ class providing the R interpreter is offered by this package
 making it easier to have "R inside" your C++ application. As R itself
 is embedded into your application, a shared library build of R is
 required. This works on Linux, OS X and even on Windows provided you
 use the same tools used to build R itself. Numerous examples are
 provided in the nine subdirectories of the examples/ directory of
 the installed package: standard, 'mpi' (for parallel computing), 'qt'
 (showing how to embed 'RInside' inside a Qt GUI application), 'wt'
 (showing how to build a "web-application" using the Wt toolkit),
 'armadillo' (for 'RInside' use with 'RcppArmadillo'), 'eigen' (for
 'RInside' use with 'RcppEigen'), and 'c_interface' for a basic C
 interface and 'Ruby' illustration.  The examples use 'GNUmakefile(s)'
 with GNU extensions, so a GNU make is required (and will use the
 'GNUmakefile' automatically). 'Doxygen'-generated documentation of
 the C++ classes is available at the 'RInside' website as well.

Functions to calculate several ecological indices of individual 
    and population niche width (Araujo's E, clustering and pairwise similarity 
    among individuals, IS, Petraitis' W, and Roughgarden's WIC/TNW) to assess 
    individual specialization based on data of resource use. Resource use can 
    be quantified by counts of categories, measures of mass or length, or 
    proportions. Monte Carlo resampling procedures are available for hypothesis 
    testing against multinomial null models.
    Details are provided in Zaccarelli et al. (2013) <doi:10.1111/2041-210X.12079>
    and associated references.

The IntCal20 radiocarbon calibration curves (Reimer et al. 2020 <doi:10.1017/RDC.2020.68>) are provided as a data package, together with previous IntCal curves (IntCal13, IntCal09, IntCal04, IntCal98), other curves (e.g., NOTCal04 [van der Plicht et al. 2004], Arnold & Libby 1951, Stuiver & Suess 1966, Pearson & Stuiver 1986) and postbomb curves. Also provided are functions to copy the curves into memory, and to read, query and plot the data underlying the IntCal20 curves.