Allows the user to implement a dark/light toggle mode in 'shiny' using the 'Nightly' 'JavaScript' library. 
    The default mode is dark/light however the user can also specify other colours.

Perform the complete processing of a set of proton nuclear magnetic resonance spectra from the free induction decay (raw data) and based on a processing sequence (macro-command file). An additional file specifies all the spectra to be considered by associating their sample code as well as the levels of experimental factors to which they belong. More detail can be found in Jacob et al. (2017) <doi:10.1007/s11306-017-1178-y>.

Implementation of a Recurrent Neural Network architectures in native R, including Long Short-Term Memory (Hochreiter and Schmidhuber, <doi:10.1162/neco.1997.9.8.1735>), Gated Recurrent Unit (Chung et al.) and vanilla RNN.

The Nearest Neighbor Descent method for finding approximate
    nearest neighbors by Dong and co-workers (2010)
    <doi:10.1145/1963405.1963487>. Based on the 'Python' package
    'PyNNDescent' <https://github.com/lmcinnes/pynndescent>.

A proof of concept implementation of regularized non-negative matrix factorization optimization.
    A non-negative matrix factorization factors non-negative matrix Y approximately as L R, for non-negative
    matrices L and R of reduced rank. This package supports such factorizations with weighted objective and
    regularization penalties. Allowable regularization penalties include L1 and L2 penalties on L and R,
    as well as non-orthogonality penalties. This package provides multiplicative update algorithms, which are
    a modification of the algorithm of Lee and Seung (2001)
    <http://papers.nips.cc/paper/1861-algorithms-for-non-negative-matrix-factorization.pdf>, as well
    as an additive update derived from that multiplicative update.  See also Pav (2004) <doi:10.48550/arXiv.2410.22698>.

Inverse normal transformation (INT) based genetic association testing. These tests are recommend for continuous traits with non-normally distributed residuals. INT-based tests robustly control the type I error in settings where standard linear regression does not, as when the residual distribution exhibits excess skew or kurtosis. Moreover, INT-based tests outperform standard linear regression in terms of power. These tests may be classified into two types. In direct INT (D-INT), the phenotype is itself transformed. In indirect INT (I-INT), phenotypic residuals are transformed. The omnibus test (O-INT) adaptively combines D-INT and I-INT into a single robust and statistically powerful approach. See McCaw ZR, Lane JM, Saxena R, Redline S, Lin X. "Operating characteristics of the rank-based inverse normal transformation for quantitative trait analysis in genome-wide association studies" <doi:10.1111/biom.13214>.