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.

It enables the identification of sequentialexperimentation orders for factorial designs that jointly reduce bias and the number of level changes. The method used is that presented by Conto et al. (2025), known as the Assignment-Expansion method, which consists of adapting the linear programming assignment problem to generate balanced experimentation orders. The properties identified are then generalized to designs with a larger number of factors and levels using the expansion method proposed by Correa et al. (2009) and later generalized by Bhowmik et al. (2017). For more details see Conto et al. (2025) <doi:10.1016/j.cie.2024.110844>, Correa et al. (2009) <doi:10.1080/02664760802499337> and Bhowmik et al. (2017) <doi:10.1080/03610926.2016.1152490>.

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.