Applies sequential clustering algorithm to animal location data 
    based on user-defined parameters. Plots interactive cluster maps and 
    provides a summary dataframe with attributes for each cluster commonly
    used as covariates in subsequent modeling efforts. Additional functions
    provide individual keyhole markup language plots for quick assessment,
    and export of global positioning system exchange format files for
    navigation purposes. 
    Methods can be found at <doi:10.1111/2041-210X.13572>.

Provides Gaussian process (GP) regression tools for social
    science inference problems.  GPs combine flexible nonparametric
    regression with principled uncertainty quantification: rather than
    committing to a single model fit, the posterior reflects lesser
    knowledge at the edge of or beyond the observed data, where other
    approaches become highly model-dependent.  The package reduces
    user-chosen hyperparameters from three to zero and supplies
    convenience functions for regression discontinuity (gp_rdd()),
    interrupted time-series (gp_its()), and general GP fitting
    (gpss(), gp_train(), gp_predict()).  Methods are described in
    Cho, Kim, and Hazlett (2026) <doi:10.1017/pan.2026.10032>.

Generalized promotion time cure model (GPTCM) via Bayesian hierarchical modeling for multiscale data integration (Zhao et al. (2025) <doi:10.48550/arXiv.2509.01001>). The Bayesian GPTCMs are applicable for both low- and high-dimensional data.

Gaussian processes are flexible distributions to model functional data. Whilst
    theoretically appealing, they are computationally cumbersome except for small datasets.
    This package implements two methods for scaling Gaussian process inference in 'Stan'. First, a
    sparse approximation of the likelihood that is generally applicable and, second, an exact method
    for regularly spaced data modeled by stationary kernels using fast Fourier methods. Utility
    functions are provided to compile and fit 'Stan' models using the 'cmdstanr' interface.
    References: Hoffmann and Onnela (2025) <doi:10.18637/jss.v112.i02>.