The user first provides design vectors n, a and b as well as null (p0) and alternative (p1) benchmark values for the probability of success. The key function "mv.plots.SM()" calculates mean values of exact upper and lower limits based on four different rank ordering methods. These plots form the basis of selecting a rank ordering. The function "inference()" calculates exact limits from a provided realisation and ordering choice. For more information, see "Exact confidence limits after a group sequential single arm binary trial" by Lloyd, C.J. (2020), Statistics in Medicine, Volume 38, 2389-2399, <doi:10.1002/sim.8909>.

Bindings to Google's C++ library Compact Language Detector 2
    (see <https://github.com/cld2owners/cld2#readme> for more information). Probabilistically
    detects over 80 languages in plain text or HTML. For mixed-language input it returns the
    top three detected languages and their approximate proportion of the total classified 
    text bytes (e.g. 80% English and 20% French out of 1000 bytes). There is also a 'cld3'
    package on CRAN which uses a neural network model instead.

Google's Compact Language Detector 3 is a neural network model for language 
    identification and the successor of 'cld2' (available from CRAN). The algorithm is still
    experimental and takes a novel approach to language detection with different properties
    and outcomes. It can be useful to combine this with the Bayesian classifier results 
    from 'cld2'. See <https://github.com/google/cld3#readme> for more information.

Contains a time series classification method that obtains a set of filters that maximize the between-class and minimize the within-class distances.

A wrapper around the new 'cleaner' package, that allows
  data cleaning functions for classes 'logical', 'factor', 'numeric', 
  'character', 'currency' and 'Date' to make data cleaning fast and
  easy. Relying on very few dependencies, it provides smart guessing,
  but with user options to override anything if needed.