The main function kcca implements a general framework for
  k-centroids cluster analysis supporting arbitrary distance measures
  and centroid computation. Further cluster methods include hard
  competitive learning, neural gas, and QT clustering. There are
  numerous visualization methods for cluster results (neighborhood
  graphs, convex cluster hulls, barcharts of centroids, ...), and
  bootstrap methods for the analysis of cluster stability.

An implementation of multiple regression models for 
    count data. These include various forms of the negative binomial (NB-1, 
    NB-2, NB-P, generalized negative binomial, etc.), Poisson-Lognormal, other
    compound Poisson distributions, the Generalized Waring model, etc.
    Information on the different forms of the negative binomial are
    described by Greene (2008) <doi:10.1016/j.econlet.2007.10.015>. For
    treatises on count models, see Cameron and Trivedi (2013) 
    <doi:10.1017/CBO9781139013567> and Hilbe (2012)
    <doi:10.1017/CBO9780511973420>. For the implementation of
    under-reporting in count models, see Wood et al. (2016)
    <doi:10.1016/j.aap.2016.06.013>. For prediction methods in random
    parameter models, see Wood and Gayah (2025)
    <doi:10.1016/j.aap.2025.108147>. For estimating random parameters using
    maximum simulated likelihood, see Greene and Hill (2010)
    <doi:10.1108/S0731-9053(2010)26>; Gourieroux and Monfort (1996)
    <doi:10.1093/0198774753.001.0001>; or Hensher et al. (2015)
    <doi:10.1017/CBO9781316136232>.

Allows maximum likelihood fitting of cluster-weighted models, a class of mixtures of regression models with random covariates. 
            Methods are described in Angelo Mazza, Antonio Punzo, Salvatore Ingrassia (2018) <doi:10.18637/jss.v086.i02>.

Format for converting an R Markdown document to a grid
    oriented dashboard. The dashboard flexibly adapts the size of it's
    components to the containing web page.

Provides tools to work with the Flexible Dirichlet
    distribution. The main features are an E-M algorithm for computing the maximum
    likelihood estimate of the parameter vector and a function based on conditional
    bootstrap to estimate its asymptotic variance-covariance matrix. It contains
    also functions to plot graphs, to generate random observations and to handle
    compositional data.