Implements the Beta Kernel Process (BKP) for nonparametric modeling of
 covariate-dependent binomial probabilities, and the Dirichlet Kernel Process (DKP) for
 categorical or multinomial response data. Scalable global-local approximations
 are provided through TwinBKP and TwinDKP, using twinning-selected global subsets
 and local nearest-neighbour updates.
 Functions are included for model fitting, predictive inference with
 uncertainty quantification, posterior simulation,
 and visualization in one- and two-dimensional input spaces.
 Gaussian, Matern 5/2, Matern 3/2, and Wendland kernels are supported,
 with hyperparameters selected by multi-start derivative-free optimization.
 For more details, see Zhao, Qing, and Xu (2025) <doi:10.48550/arXiv.2508.10447>.

Bayesian kernel projection classifier (Domijan and Wilson,2011) <doi:10.1007/s11222-009-9161-8> is a nonlinear multicategory classifier which performs the classification of the projections of the data to the principal axes of the feature space. A Gibbs sampler is implemented to find the posterior distributions of the parameters.

Facilitates scalable spatiotemporally varying coefficient
    modelling with Bayesian kernelized tensor regression.
    The important features of this package are:
    (a) Enabling local temporal and spatial modeling of the relationship between
    the response variable and covariates.
    (b) Implementing the model described by Lei et al. (2023) <doi:10.48550/arXiv.2109.00046>.
    (c) Using a Bayesian Markov Chain Monte Carlo (MCMC) algorithm to sample from the posterior
    distribution of the model parameters.
    (d) Employing a tensor decomposition to reduce the number of estimated parameters.
    (e) Accelerating tensor operations and enabling graphics processing unit (GPU) acceleration
    with the 'torch' package.

Fits boundary line models to datasets as proposed by Webb
    (1972) <doi:10.1080/00221589.1972.11514472> and makes statistical
    inferences about their parameters. Provides additional tools for
    testing datasets for evidence of boundary presence and selecting
    initial starting values for model optimization prior to fitting the
    boundary line models. It also includes tools for conducting post-hoc
    analyses such as predicting boundary values and identifying the most
    limiting factor (Miti, Milne, Giller, Lark (2024)
    <doi:10.1016/j.fcr.2024.109365>). This ensures a comprehensive
    analysis for datasets that exhibit upper boundary structures.