Bindings to 'Uno' (Unifying Nonlinear Optimization), a C++ solver
    for smooth nonlinearly constrained optimization. 'Uno' unifies Lagrange-Newton
    methods, including sequential quadratic programming and interior-point
    methods, by decomposing them into interacting building blocks
    (constraint-relaxation, inequality-handling, Hessian, and globalization
    strategies) that can be freely combined, either through options or through
    presets that reproduce established solvers such as 'filterSQP' and 'IPOPT'.
    The framework is described in Vanaret and Leyffer (2024)
    <doi:10.48550/arXiv.2406.13454>.

Assess the significance of identified clusters and estimates the true number of clusters by comparing the explained variation due to the clustering from the original data to that produced by clustering a unimodal reference distribution which preserves the covariance structure in the data. The reference distribution is generated using kernel density estimation and a Gaussian copula framework. A dimension reduction strategy and sparse covariance estimation optimize this method for the high-dimensional, low-sample size setting. This method is described in Helgeson, Vock, and Bair (2021) <doi:10.1111/biom.13376>.

Tools for converting data from complex or irregular layouts to a
    columnar structure.  For example, tables with multilevel column or row
    headers, or spreadsheets.  Header and data cells are selected by their
    contents and position, as well as formatting and comments where available,
    and are associated with one other by their proximity in given directions.
    Functions for data frames and HTML tables are provided.

Methods for managing under- and over-enrollment in Simon's Two-Stage Design are offered by providing adaptive threshold adjustments and sample size recalibration. It also includes post-inference analysis tools to support clinical trial design and evaluation. The package is designed to enhance flexibility and accuracy in trial design, ensuring better outcomes in oncology and other clinical studies. Yunhe Liu, Haitao Pan (2024). Submitted.