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Select optimal functional regression or
       dichotomized quantile predictors for
       survival/logistic/numeric outcome and perform
       optimistic bias correction for any optimally
       dichotomized numeric predictor(s), as in Yi, et. al.
       (2023) <doi:10.1016/j.labinv.2023.100158>.

Nonlinear machine learning tool for classification, clustering
        and dimensionality reduction. It integrates 12 q-kernel functions and
        15 conditional negative definite kernel functions and includes the 
        q-kernel and conditional negative definite kernel version of
        density-based spatial clustering of applications with noise,
        spectral clustering, generalized discriminant analysis, principal
        component analysis, multidimensional scaling, locally linear embedding,
        sammon's mapping and t-Distributed stochastic neighbor embedding.

'QuantLib' bindings are provided for R using 'Rcpp' via an evolved version
 of the initial header-only 'Quantuccia' project offering an subset of 'QuantLib' (now
 maintained separately just for the calendaring subset). See the included file 'AUTHORS'
 for a full list of contributors to 'QuantLib' (and hence also 'Quantuccia').

Functionality to read, recode, and transcode data as used in 
  quantitative language comparison, specifically to deal with multilingual 
  orthographic variation (Moran & Cysouw (2018) <doi:10.5281/zenodo.1296780>) 
  and with the recoding of nominal data.

Extension of the functionality of the 'Matrix' package for using sparse matrices. Some of the functions are very general, while other are highly specific for special data format as used for quantitative language comparison.

Collection of visualizations as used in quantitative language 
  comparison. Currently implemented are visualisations dealing nominal data with 
  multiple levels ("level map" and "factor map"), and assistance for making 
  weighted geographical Voronoi-maps ("weighted map").