Focused on extracting important data from track points such as speed, distance, elevation difference and azimuth.(PLAZA, J. et al., 2022) <doi:10.1016/j.applanim.2022.105643>.

Implements the generalized propensity score cumulative distribution
    function proposed by Greene (2017)
    <https://digitalcommons.library.tmc.edu/dissertations/AAI10681743/>.
    A single scalar balancing score is calculated for any generalized propensity
    score vector with three or more treatments. This balancing score is used for
    propensity score matching and stratification in outcome analyses when analyzing
    either ordinal or multinomial treatments.

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>.

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>.