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.

Processes standard recommendation datasets (e.g., a user-item rating matrix) as input and generates rating predictions and lists of recommended items. Standard algorithm implementations which are included in this package are the following: Global/Item/User-Average baselines, Weighted Slope One, Item-Based KNN, User-Based KNN, FunkSVD, BPR and weighted ALS. They can be assessed according to the standard offline evaluation methodology (Shani, et al. (2011) <doi:10.1007/978-0-387-85820-3_8>) for recommender systems using measures such as MAE, RMSE, Precision, Recall, F1, AUC, NDCG, RankScore and coverage measures. The package (Coba, et al.(2017) <doi: 10.1007/978-3-319-60042-0_36>) is intended for rapid prototyping of recommendation algorithms and education purposes.

'IUCN' Red List (<https://api.iucnredlist.org/>) client.
    The 'IUCN' Red List is a global list of threatened and endangered species.
    Functions cover all of the Red List 'API' routes. An 'API' key is required.

Superclasses 'PostgreSQL' connection to help enable full 'dplyr' functionality on 'Redshift'.