A genomic simulation approach for creating biologically
    informed individual genotypes from empirical data that 1) samples alleles
    from populations without replacement, 2) segregates alleles based on species-specific
    recombination rates. 'gscramble' is a flexible simulation approach that allows users
    to create pedigrees of varying complexity in order to simulate admixed genotypes.
    Furthermore, it allows users to track haplotype blocks from the source populations
    through the pedigrees.

Graph signals residing on the vertices of a graph have recently gained prominence in research in various fields. Many methodologies have been proposed to analyze graph signals by adapting classical signal processing tools. Recently, several notable graph signal decomposition methods have been proposed, which include graph Fourier decomposition based on graph Fourier transform, graph empirical mode decomposition, and statistical graph empirical mode decomposition. This package efficiently implements multiscale analysis applicable to various fields, and offers an effective tool for visualizing and decomposing graph signals. For the detailed methodology, see Ortega et al. (2018) <doi:10.1109/JPROC.2018.2820126>, Shuman et al. (2013) <doi:10.1109/MSP.2012.2235192>, Tremblay et al. (2014) <https://www.eurasip.org/Proceedings/Eusipco/Eusipco2014/HTML/papers/1569922141.pdf>, and Cho et al. (2024) "Statistical graph empirical mode decomposition by graph denoising and boundary treatment".

The gene-set distance analysis of omic data is implemented by generalizing distance correlations to evaluate the association of a gene set with categorical and censored event-time variables.

Derives group sequential clinical trial designs and describes
    their properties. Particular focus on time-to-event, binary, and
    continuous outcomes. Largely based on methods described in
    Jennison, Christopher and Turnbull, Bruce W., 2000,
    "Group Sequential Methods with Applications to Clinical Trials"
    ISBN: 0-8493-0316-8.