Supply implementation to model generalized multivariate functional
 data using Bayesian additive mixed models of R package 'bamlss' via a latent
 Gaussian process (see Umlauf, Klein, Zeileis (2018) 
 <doi:10.1080/10618600.2017.1407325>).

Some methods for the inference and clustering of univariate and 
             multivariate functional data, using a generalization of Mahalanobis
             distance, along with some functions useful for the analysis of functional data.
             For further details, see Martino A., Ghiglietti, A., Ieva, F. and Paganoni A. M. (2017) <arXiv:1708.00386>.

Support for geostatistical analysis of multivariate data, 
         in particular data with restrictions, e.g. positive amounts, 
         compositions, distributional data, microstructural data, etc. 
         It includes descriptive analysis and modelling for such data, both 
         from a two-point Gaussian perspective and multipoint perspective.
         The methods mainly follow Tolosana-Delgado, Mueller and van den
         Boogaart (2018) <doi:10.1007/s11004-018-9769-3>.

Gaussian mixture graphical models include Bayesian networks and
    dynamic Bayesian networks (their temporal extension) whose local probability
    distributions are described by Gaussian mixture models. They are powerful
    tools for graphically and quantitatively representing nonlinear dependencies
    between continuous variables. This package provides a complete framework to
    create, manipulate, learn the structure and the parameters, and perform
    inference in these models. Most of the algorithms are described in the PhD
    thesis of Roos (2018) <https://tel.archives-ouvertes.fr/tel-01943718>.

Utilities for common medical-statistics graphics
    and tables, including automatic creation of publication-ready "Table 1"
    summaries; transition (Sankey) plots; flow-chart helpers that extend the
    grid package; Bézier lines and arrows; and a singular-value-decomposition-
    based variable-selection method.