Textual statistics functions formerly in the 'quanteda' package.
    Textual statistics for characterizing and comparing textual data. Includes 
    functions for measuring term and document frequency, the co-occurrence of 
    words, similarity and distance between features and documents, feature entropy, 
    keyword occurrence, readability, and lexical diversity.  These functions 
    extend the 'quanteda' package and are specially designed for sparse textual data.

Provides different functions for quantifying qualitative survey data. It supports the Carlson-Parkin method, the regression approach, the balance approach and the conditional expectations method.

Code for centroid, median and quantile classifiers.

Functions for simulation, estimation, and model
       selection of finite mixtures of Tukey g-and-h
       distributions.

Based on Alan D. Hutson (1999) <doi:10.1080/02664769922458>, "Calculating nonparametric confidence intervals for quantiles using fractional order statistics", Journal of Applied Statistics, 26:3, 343-353.

Estimation and inference methods for the cross-quantilogram.
    The cross-quantilogram is a measure of nonlinear dependence between
    two variables, based on either unconditional or conditional quantile
    functions.  The cross-quantilogram can be considered as an extension
    of the correlogram, which is a correlation function over multiple lag
    periods and mainly focuses on linear dependency.  One can use the
    cross-quantilogram to detect the presence of directional
    predictability from one time series to another.  This package provides
    a statistical inference method based on the stationary bootstrap.  See
    Linton and Whang (2007) <doi:10.1016/j.jeconom.2007.01.004> for
    univariate time series analysis and Han, Linton, Oka and Whang (2016)
    <doi:10.1016/j.jeconom.2016.03.001> for multivariate time series
    analysis.

Integration of the 'units' and 'errors' packages for a complete
    quantity calculus system for R vectors, matrices and arrays, with automatic
    propagation, conversion, derivation and simplification of magnitudes and
    uncertainties. Documentation about 'units' and 'errors' is provided in the
    papers by Pebesma, Mailund & Hiebert (2016, <doi:10.32614/RJ-2016-061>) and
    by Ucar, Pebesma & Azcorra (2018, <doi:10.32614/RJ-2018-075>), included in
    those packages as vignettes; see 'citation("quantities")' for details.