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.

A statistical disclosure control tool to protect tables by suppression 
    using the Gaussian elimination secondary suppression algorithm. 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.

A collection of algorithms and functions 
  for fitting data to a generalized lambda distribution 
  via moment matching methods, and generalized 
  bootstrapping.

Package GB2 explores the Generalized Beta distribution of the second kind. Density, cumulative distribution function, quantiles and moments of the distributions are given. Functions for the full log-likelihood, the profile log-likelihood and the scores are provided. Formulas for various indicators of inequality and poverty under the GB2 are implemented. The GB2 is fitted by the methods of maximum pseudo-likelihood estimation using the full and profile log-likelihood, and non-linear least squares estimation of the model parameters. Various plots for the visualization and analysis of the results are provided. Variance estimation of the parameters is provided for the method of maximum pseudo-likelihood estimation. A mixture distribution based on the compounding property of the GB2 is presented (denoted as "compound" in the documentation). This mixture distribution is based on the discretization of the distribution of the underlying random scale parameter. The discretization can be left or right tail. Density, cumulative distribution function, moments and quantiles for the mixture distribution are provided. The compound mixture distribution is fitted using the method of maximum pseudo-likelihood estimation. The fit can also incorporate the use of auxiliary information. In this new version of the package, the mixture case is complemented with new functions for variance estimation by linearization and comparative density plots.