Provides functions to retrieve headers, titles, and footnotes
    from structured metadata sources (e.g., Excel or CSV files) for
    annotating tables, listings, and figures in clinical study reports
    (CSRs) or other formal deliverables. It supports separation of
    metadata from analysis code in clinical reporting workflows.

In putative Transcription Factor Binding Sites (TFBSs) 
             identification from sequence/alignments,
             we are interested in the significance of certain match score.
             TFMPvalue provides the accurate calculation of P-value with 
             score threshold for Position Weight Matrices, 
             or the score with given P-value. 
             It is an interface to code originally made available by 
             Helene Touzet and Jean-Stephane Varre, 2007, 
             Algorithms Mol Biol:2, 15. <doi:10.1186/1748-7188-2-15>.

Provides a framework for the creation and use of Neural ordinary
    differential equations with the 'tensorflow' and 'keras' packages. 
    The idea of Neural ordinary differential equations comes from 
    Chen et al. (2018) <doi:10.48550/arXiv.1806.07366>, and 
    presents a novel way of learning and solving differential systems.

The eigenvalues of observed symmetric matrices are often of intense scientific interest. This package offers single sample tests for the eigenvalues of the population mean or the eigenvalue-multiplicity of the population mean. For k-samples, this package offers tests for equal eigenvalues between samples. Included is support for matrices with constraints common to geophysical tensors (constant trace, sum of squared eigenvalues, or both) and eigenvectors are usually considered nuisance parameters. Pivotal bootstrap methods enable these tests to have good performance for small samples (n=15 for 3x3 matrices). These methods were developed and studied by Hingee, Scealy and Wood (2026, <doi:10.1080/01621459.2025.2606381>). Also available is a 2-sample test using a Gaussian orthogonal ensemble approximation and an eigenvalue-multiplicity test that assumes orthogonally-invariant covariance.