Robust Location and Scatter Estimation and Robust
        Multivariate Analysis with High Breakdown Point:
        principal component analysis (Filzmoser and Todorov (2013), <doi:10.1016/j.ins.2012.10.017>),
        linear and quadratic discriminant analysis (Todorov and Pires (2007)),
        multivariate tests (Todorov and Filzmoser (2010) <doi:10.1016/j.csda.2009.08.015>),
        outlier detection (Todorov et al. (2010) <doi:10.1007/s11634-010-0075-2>).
        See also Todorov and Filzmoser (2009) <urn:isbn:978-3838108148>,
        Todorov and Filzmoser (2010) <doi:10.18637/jss.v032.i03> and
        Boudt et al. (2019) <doi:10.1007/s11222-019-09869-x>.

Provides methods for multiway data analysis by means of Parafac
    and Tucker 3 models. Robust versions (Engelen and Hubert (2011) <doi:10.1016/j.aca.2011.04.043>) and versions
    for compositional data are also provided (Gallo (2015) <doi:10.1080/03610926.2013.798664>, Di Palma et al. (2018) <doi:10.1080/02664763.2017.1381669>).
    Several optimization methods alternative to ALS are available 
    (Simonacci and Gallo (2019) <doi:10.1016/j.chemolab.2019.103822>, Simonacci and Gallo (2020) <doi:10.1007/s00500-019-04320-9>).

Robust multivariate methods for high dimensional data including
        outlier detection (Filzmoser and Todorov (2013) <doi:10.1016/j.ins.2012.10.017>), 
        robust sparse PCA (Croux et al. (2013) <doi:10.1080/00401706.2012.727746>, Todorov and Filzmoser (2013) <doi:10.1007/978-3-642-33042-1_31>), 
        robust PLS (Todorov and Filzmoser (2014) <doi:10.17713/ajs.v43i4.44>), 
        and robust sparse classification (Ortner et al. (2020) <doi:10.1007/s10618-019-00666-8>).

Robust Location and Scatter Estimation and Robust
        Multivariate Analysis with High Breakdown Point for Incomplete
        Data (missing values) (Todorov et al. (2010) <doi:10.1007/s11634-010-0075-2>).

Efficient framework for ridge redundancy analysis (rrda),
    tailored for high-dimensional omics datasets where the number of
    predictors exceeds the number of samples. The method leverages
    Singular Value Decomposition (SVD) to avoid direct inversion of the
    covariance matrix, enhancing scalability and performance. It also
    introduces a memory-efficient storage strategy for coefficient
    matrices, enabling practical use in large-scale applications. The
    package supports cross-validation for selecting regularization
    parameters and reduced-rank dimensions, making it a robust and
    flexible tool for multivariate analysis in omics research. Please
    refer to our article (Yoshioka et al., 2025) for more details.