Base S4-classes and functions for robust asymptotic statistics.

Includes 'sysdata.rda' file for packages of the 'RobASt' - family of packages; is
            currently used by package 'RobExtremes' only.

Implementation of a variety of methods to compute
    the robustness of ecological interaction networks with binary interactions 
    as described in <doi:10.1002/env.2709>. In particular, using the Stochastic 
    Block Model and its bipartite counterpart, the Latent Block Model to put a 
    parametric model on the network, allows the comparison of the robustness of 
    networks differing in species richness and number of interactions. It also
    deals with networks that are partially sampled and/or with missing values.

Robust categorical data analysis based on the theory of C-estimation developed in Welz (2024) <doi:10.48550/arXiv.2403.11954>. For now, the package only implements robust estimation of polychoric correlation as proposed in Welz, Mair and Alfons (2024) <doi:10.48550/arXiv.2407.18835> with methods for printing and plotting. We will implement further models in future releases. In addition, the package is still experimental, so input arguments and class structure may change in future releases.

Methods for analysis of compositional data including robust
    methods  (<doi:10.1007/978-3-319-96422-5>), imputation of missing values (<doi:10.1016/j.csda.2009.11.023>), methods to replace 
    rounded zeros (<doi:10.1080/02664763.2017.1410524>, <doi:10.1016/j.chemolab.2016.04.011>, <doi:10.1016/j.csda.2012.02.012>), 
    count zeros (<doi:10.1177/1471082X14535524>), 
    methods to deal with essential zeros (<doi:10.1080/02664763.2016.1182135>), (robust) outlier
    detection for compositional data, (robust) principal component analysis for
    compositional data, (robust) factor analysis for compositional data, (robust)
    discriminant analysis for compositional data (Fisher rule), robust regression
    with compositional predictors, functional data analysis (<doi:10.1016/j.csda.2015.07.007>) and p-splines (<doi:10.1016/j.csda.2015.07.007>), 
    contingency (<doi:10.1080/03610926.2013.824980>) 
    and compositional tables (<doi:10.1111/sjos.12326>, <doi:10.1111/sjos.12223>, <doi:10.1080/02664763.2013.856871>) 
    and (robust) Anderson-Darling normality tests for
    compositional data as well as popular log-ratio transformations (addLR, cenLR,
    isomLR, and their inverse transformations). In addition, visualisation and
    diagnostic tools are implemented as well as high and low-level plot functions
    for the ternary diagram.