Predicts statistics of a reference distribution from a mixture of raw clinical measurements (healthy and pathological). Uses pretrained CNN models to estimate the mean, standard deviation, and reference fraction from 1D or 2D sample data. Methods are described in LeBien, Velev, and Roche-Lima (2026) "RINet: synthetic data training for indirect estimation of clinical reference distributions" <doi:10.1016/j.jbi.2026.104980>.

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

Branching process simulation model of infectious disease
    transmission with flexible parameterisation of epidemiology and
    targeted interventions, including isolation, contact tracing and
    quarantine, to reduce transmission, together with functions to
    evaluate outbreak control.  Introduced in Hellewell et al. (2020)
    <doi:10.1016/S2214-109X(20)30074-7>.

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

Rank-based, asymptotic distribution-free change-point detection for
    modern (high-dimensional, non-Euclidean) data, based on the graph-induced
    ranking scheme of Zhou and Chen (2025) <doi:10.1109/TIT.2025.3575858>.
    Given a rank matrix built from a pairwise similarity, the method scans for a single
    change-point or a changed interval using three statistics (weighted 'WR',
    max-type 'MR', and generalized 'TR') and returns analytic distribution-free
    p-value approximations (with an optional skewness correction) as well as
    optional permutation p-values.

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.