Gene and exon information from Ensembl genome builds GRCh38.p13 (104) and GRCh37 (v40) to use with the 'topr' package.

Evaluation of alternatives based on multiple criteria using TOPSIS method.

Assists in the TOPSIS analysis process, designed to return at the end of the answer of the TOPSIS multicriteria analysis,  a ranking table with the best option as the analysis proposes. TOPSIS is basically a technique developed by Hwang and Yoon in 1981, starting from the point that the best alternative should be closest to the positive ideal solution and farthest from the negative one, based on several criteria to result in the best benefit. (LIU, H. et al., 2019) <doi:10.1016/j.agwat.2019.105787>.

The goal of tor (to-R) is to help you to import multiple
    files from a single directory at once, and to do so as quickly,
    flexibly, and simply as possible.

Provides functionality to define and train neural networks similar to
    'PyTorch' by Paszke et al (2019) <doi:10.48550/arXiv.1912.01703> but written entirely in R
    using the 'libtorch' library. Also supports low-level tensor operations and
    'GPU' acceleration.

Provides access to datasets, models and processing
    facilities for deep learning in audio.

Provides datasets in a format that can be easily consumed by torch 'dataloaders'.
  Handles data downloading from multiple sources, caching and pre-processing so
  users can focus only on their model implementations.

Optimizers for 'torch' deep learning library. These
    functions include recent results published in the literature and are
    not part of the optimizers offered in 'torch'. Prospective users
    should test these optimizers with their data, since performance
    depends on the specific problem being solved.  The packages includes
    the following optimizers: (a) 'adabelief' by Zhuang et al (2020),
    <arXiv:2010.07468>; (b) 'adabound' by Luo et al.(2019),
    <arXiv:1902.09843>; (c) 'adahessian' by Yao et al.(2021)
    <arXiv:2006.00719>; (d) 'adamw' by Loshchilov & Hutter (2019),
    <arXiv:1711.05101>; (e) 'madgrad' by Defazio and Jelassi (2021),
    <arXiv:2101.11075>; (f) 'nadam' by Dozat (2019),
    <https://openreview.net/pdf/OM0jvwB8jIp57ZJjtNEZ.pdf>; (g) 'qhadam' by
    Ma and Yarats(2019), <arXiv:1810.06801>; (h) 'radam' by Liu et al.
    (2019), <arXiv:1908.03265>; (i) 'swats' by Shekar and Sochee (2018),
    <arXiv:1712.07628>; (j) 'yogi' by Zaheer et al.(2019),
    <https://papers.nips.cc/paper/8186-adaptive-methods-for-nonconvex-optimization>.