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.

Enables 'tidyverse' operations on 'quanteda' corpus objects by extending 'dplyr' verbs to work directly with corpus objects and their document-level variables ('docvars'). Implements row operations for 'subsetting' and reordering documents; column operations for managing document variables; grouped operations; and two-table verbs for merging external data. For more on 'quanteda' see 'Benoit et al.' (2018) <doi:10.21105/joss.00774>. For 'dplyr' see 'Wickham et al.' (2023) <doi:10.32614/CRAN.package.dplyr>.

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.  The author has retired from academic research. 
  Accordingly, this package should not be considered a validated tool 
  for use in peer-reviewed publications or as the basis for grant applications.  
  Backward compatibility with user-code published in 
  <doi:10.1007/s11222-025-10596-9> is not maintained in versions >= 0.3.0 (July 2026) of this package. The authors of those publications are the appropriate contacts for reproducibility inquiries.

Estimation of different quantile models, at the moment only Conditional autoregressive value at risk (CAViaR) proposed by Engle & Manganelli (2004) <doi:10.1198/073500104000000370> with also the specification proposed in Huang et al. (2009) <doi:10.1016/j.eneco.2008.12.006> and it's multivariate extension, Multi-variate multi-quantile CAViaR (MVMQ-CAViaR) proposed by White et al. (2015) <doi:10.1016/j.jeconom.2015.02.004> are available, however, in further updates, other models and extensions will be included.