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Robust tail dependence estimation for bivariate models. This package is based on two papers by the authors:'Robust and bias-corrected estimation of the coefficient of tail dependence' and 'Robust and bias-corrected estimation of probabilities of extreme failure sets'. This work was supported by a research grant (VKR023480) from VILLUM FONDEN and an international project for scientific cooperation (PICS-6416).

Provides response time distributions (density/PDF,
       distribution function/CDF, quantile function, and random
       generation): (a) Ratcliff diffusion model (Ratcliff &
       McKoon, 2008, <doi:10.1162/neco.2008.12-06-420>) based on C
       code by Andreas and Jochen Voss and (b) linear ballistic
       accumulator (LBA; Brown & Heathcote, 2008,
       <doi:10.1016/j.cogpsych.2007.12.002>) with different
       distributions underlying the drift rate.

Machine learning and visualization package with an 'S7' backend 
    featuring comprehensive type checking and validation, paired with an efficient functional
    user-facing API. train(), cluster(), and decomp() provide one-call access to supervised and 
    unsupervised learning. All configuration steps are performed using setup functions and 
    validated. A single call to train() handles preprocessing, hyperparameter tuning, and testing 
    with nested resampling. Supports 'data.frame', 'data.table', and 'tibble' inputs, parallel 
    execution, and interactive visualizations. The package first appeared in E.D. Gennatas (2017)
    <https://repository.upenn.edu/entities/publication/d81892ea-3087-4b71-a6f5-739c58626d64>.

Implements the Amino Acid Annotation (A3) format using 'S7' classes.
    The A3 format is a structured schema for annotating amino acid sequences with site, region, 
    post-translational modification (PTM), processing event, and variant information. 
    Provides functions to create, read, and write A3 objects, and to import annotations from 
    'UniProt', 'AlphaFold', 'PDBe', 'ClinVar', and 'Ensembl'.