A collection of methods for the robust analysis of univariate and
    multivariate functional data, possibly in high-dimensional cases, and hence
    with attention to computational efficiency and simplicity of use. See the R 
    Journal publication of Ieva et al. (2019) <doi:10.32614/RJ-2019-032> for an 
    in-depth presentation of the 'roahd' package. See Aleman-Gomez et al. (2021) 
    <arXiv:2103.08874> for details about the concept of depthgram.

Provides an interface to the OAuth 1.0 specification
        allowing users to authenticate via OAuth to the
        server of their choice.

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.

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.