Provides functions for the flexible integration of heterogeneous scRNA-seq datasets across multiple tissue types, platforms, and experimental batches. Implements the method described in Ming (2022) <doi:10.1093/bib/bbac167>. The package incorporates modified 'C++' source code from the 'flashpca' library (Abraham, 2014-2016 <https://github.com/gabraham/flashpca>) for efficient principal component analysis, and the 'Spectra' library (Qiu, 2016-2025) for large-scale eigenvalue and singular value decomposition; see 'inst/COPYRIGHTS' for details on third-party code.

A pipeline for matching messy company name strings against a clean dictionary (e.g., 'Orbis').
    Implements a cascading strategy: Exact -> Fuzzy ('zoomerjoin') -> 'FTS5' ('SQLite') -> Rarity Weighted.
    Name normalization covers German, French, Italian and English legal forms and conventions,
    which suits multilingual registers such as the Swiss one.
    Normalization discards detail, so several dictionary entries can collapse onto one string;
    these groups are matched once and a crosswalk back to every original entry is retained.
    References: Beniamino Green (2025) <https://github.com/beniaminogreen/zoomerjoin>; <https://www.sqlite.org/fts5.html>.

A model-independent factor importance ranking and selection procedure based on total Sobol' indices. Please see Huang and Joseph (2025) <doi:10.1080/00401706.2025.2483531>. This research is supported by U.S. National Science Foundation grants DMS-2310637 and DMREF-1921873.

Income tax calculations for England, Northern Ireland and Wales. Estimate annual income tax within the different taxation bands at specified levels of both taxable income and the Personal Allowance, emulating the results obtained at <https://www.gov.uk/estimate-income-tax>. Calculate the standard Personal Allowance at various levels of taxable income. Estimate the personal allowance required to recoup a specified amount of income tax.

Proposes non-parametric estimates of the Fisher information measure and the 
              Shannon entropy power. More theoretical and implementation details can be found
              in Guignard et al. <doi:10.3389/feart.2020.00255>. A 'python' version of this
              work is available on 'github' and 'PyPi' ('FiShPy').