Performs multiple comparison analyses using Tukey's
    Honestly Significant Difference (HSD) test, with intuitive letter
    grouping of means for balanced and unbalanced designs. Accepts
    input from 'formula', 'aov', 'lm', 'aovlist', and 'lmerMod' objects,
    including straightforward handling of interactions. For more
    details see Tukey (1949) <doi:10.2307/3001913>.

Density, cumulative density, quantile and
       simulation of the 4-parameter Tukey g-and-h (1977) distributions. The
       quantile-based transformation (Hoaglin 1985
       <doi:10.1002/9781118150702.ch11>) and its reverse
       transformation.

Provides wrapper functions to the multiple marginal model function mmm() of package 'multcomp' to implement the trend test of Tukey, Ciminera and Heyse (1985) <DOI:10.2307/2530666> for general parametric models.

Integrates several popular high-dimensional methods based on Linear Discriminant Analysis (LDA) and provides a comprehensive and user-friendly toolbox for linear, semi-parametric and tensor-variate classification as mentioned in Yuqing Pan, Qing Mai and Xin Zhang (2019) <arXiv:1904.03469>. Functions are included for covariate adjustment, model fitting, cross validation and prediction.

Generate constrained Delaunay triangulation meshes for use with
    stochastic partial differential equation (SPDE) spatial models
    (Lindgren, Rue and Lindstroem 2011 <doi:10.1111/j.1467-9868.2011.00777.x>).
    Provides automatic mesh generation from point coordinates with boundary
    constraints, Ruppert refinement for mesh quality, finite element method
    (FEM) matrix assembly (mass, stiffness, projection), barrier models,
    spherical meshes via icosahedral subdivision, and metric graph meshes for
    network geometries. Built on the 'CDT' header-only C++ library
    (Amirkhanov 2024 <https://github.com/artem-ogre/CDT>). Designed as the
    mesh backend for the 'tulpa' Bayesian hierarchical modelling engine but
    usable standalone for any spatial triangulation task.