Functions to fit two-dimensional Gaussian functions, predict values from
    fits, and produce plots of predicted data via either 'ggplot2' or base R 
    plotting.

A collection of functions to perform Gaussian quadrature
        with different weight functions corresponding to the orthogonal
        polynomials in package orthopolynom.  Examples verify the
        orthogonality and inner products of the polynomials.

It is well known that the distribution of a Gaussian ratio
    does not follow a Gaussian distribution.
    The lack of awareness among users of vegetation indices about this
    non-Gaussian nature could lead to incorrect statistical modeling
    and interpretation.
    This package provides tools to accurately handle and analyse such
    ratios: density function, parameter estimation, simulation.
    An example on the study of chlorophyll fluorescence can be found in
    A. El Ghaziri et al. (2023) <doi:10.3390/rs15020528>
    and another method for parameter estimation is given in Bouhlel et al. (2023)
    <doi:10.23919/EUSIPCO58844.2023.10290111>.

A statistical disclosure control tool to protect tables by suppression 
    using the Gaussian elimination secondary suppression algorithm 
    (Langsrud, 2024) <doi:10.1007/978-3-031-69651-0_6>. A suggestion is 
    to start by working with functions SuppressSmallCounts() and 
    SuppressDominantCells(). These functions use primary suppression functions for 
    the minimum frequency rule and the dominance rule, respectively. Novel 
    functionality for suppression of disclosive cells is also included. General 
    primary suppression functions can be supplied as input to the general working 
    horse function, GaussSuppressionFromData(). Suppressed frequencies can be 
    replaced by synthetic decimal numbers as described in 
    Langsrud (2019) <doi:10.1007/s11222-018-9848-9>.

R function gawdis() produces multi-trait dissimilarity with more uniform contributions of different traits. de Bello et al. (2021) <doi:10.1111/2041-210X.13537> presented the approach based on minimizing the differences in the correlation between the dissimilarity of each trait, or groups of traits, and the multi-trait dissimilarity. This is done using either an analytic or a numerical solution, both available in the function.