A user-friendly 'shiny' application for Bayesian machine
    learning analysis of marine species distributions. GLOSSA (Global
    Ocean Species Spatio-temporal Analysis) uses Bayesian Additive
    Regression Trees (BART; Chipman, George, and McCulloch (2010)
    <doi:10.1214/09-AOAS285>) to model species distributions with
    intuitive workflows for data upload, processing, model fitting, and
    result visualization. It supports presence-absence and presence-only
    data (with pseudo-absence generation), spatial thinning,
    cross-validation, and scenario-based projections. GLOSSA is designed
    to facilitate ecological research by providing easy-to-use tools for
    analyzing and visualizing marine species distributions across
    different spatial and temporal scales. Optionally, pseudo-absences can
    be generated within the environmental space using the external package
    'flexsdm' (not on CRAN), which can be downloaded from
    <https://github.com/sjevelazco/flexsdm>; this functionality is used
    conditionally when available and all core features work without it.

Add glossaries to markdown and quarto documents by tagging individual words. Definitions can be provided inline or in a separate file.

Read examples with interlinear glosses from files
    or from text and print them in a way compatible with both
    Latex and HTML outputs.

Provides a framework for creating plots with glowing points.

R Interface to C API of GLPK, depends on GLPK Version >= 4.42.

Likelihood ratio tests for genome-wide association and genome-wide linkage analysis under heterogeneity.

Provides a fully automated workflow for calibrating and analyzing
    light-level geolocation ('GLS') data from seabirds and other wildlife. The
    'glscalibrator' package auto-discovers birds from directory structures,
    automatically detects calibration periods from the first days of deployment,
    processes multiple individuals in batch mode, and generates standardized
    outputs including position estimates, diagnostic plots, and quality control
    metrics. Implements the established threshold workflow internally, following
    the methods described in 'SGAT' (Wotherspoon et al. (2016)
    <https://github.com/SWotherspoon/SGAT>), 'GeoLight' (Lisovski et al. (2012)
    <doi:10.1111/j.2041-210X.2012.00185.x>), and 'TwGeos' (Lisovski et al.
    (2019) <https://github.com/slisovski/TwGeos>).