CHAP-GWAS (Chromosomal Haplotype-Integrated Genome-Wide Association
    Study) provides a dynamically adaptive framework for genome-wide association
    studies (GWAS) that integrates chromosome-scale haplotypes with single
    nucleotide polymorphism (SNP) analysis. The method identifies and extends
    haplotype variants based on their phenotypic associations rather than
    predefined linkage blocks, enabling high-resolution detection of quantitative
    trait loci (QTL). By leveraging long-range phased haplotype information,
    CHAP-GWAS improves statistical power and offers a more comprehensive view of
    the genetic architecture underlying complex traits.

An end-to-end framework that enables users to implement various descriptive studies for a given set of target and outcome cohorts for data mapped to the Observational Medical Outcomes Partnership Common Data Model.

A program for reconstructing local fire histories from
    high-resolution, continuously sampled lake-sediment charcoal records.
    'CharAnalysis' decomposes a charcoal record into low- and high-frequency
    components and uses locally defined thresholds to separate fire signal
    from noise, following the approach of Higuera et al. (2009)
    <doi:10.1890/07-2019.1>, with underlying assumptions and rationale
    described in Higuera et al. (2010) <doi:10.1071/WF09134>. The package is
    designed for macroscopic charcoal records with contiguous sampling fine
    enough to resolve individual fire events, and is not appropriate for
    low-resolution or discontinuously sampled records. See the package URL
    for the User's Guide and application examples.

Creates a new chars class which looks like a string but is actually 
    a vector of individual characters, making 'strings' iterable. This class 
    enables vector operations on 'strings' such as reverse, sort, head, and set 
    operations.

Provides a standardized and reproducible framework for characterizing
    and classifying discrete color classes from digital images of biological organisms.
    The package automatically determines the presence or absence of 10 human-visible
    color categories (black, blue, brown, green, grey, orange, purple, red, white, yellow)
    using a biologically-inspired Color Look-Up Table (CLUT) that partitions HSV color space.
    Supports both fully automated and semi-automated (interactive) workflows with complete
    provenance tracking for reproducibility. Pre-processes images using the 'recolorize'
    package (Weller et al. 2024 <doi:10.1111/ele.14378>) for spatial-color binning, and
    integrates with 'pavo' (Maia et al. 2019 <doi:10.1111/2041-210X.13174>) for color
    pattern geometry statistics. Designed for high-throughput analysis and seamless
    integration with downstream evolutionary analyses.