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.

Performs prediction of a response function from simulated response values, allowing black-box optimization of functions estimated with some error. Includes a simple user interface for such applications, as well as more specialized functions designed to be called by the Migraine software (Rousset and Leblois, 2012 <doi:10.1093/molbev/MSR262>; Leblois et al., 2014 <doi:10.1093/molbev/msu212>; and see URL). The latter functions are used for prediction of likelihood surfaces and implied likelihood ratio confidence intervals, and for exploration of predictor space of the surface. Prediction of the response is based on ordinary Kriging (with residual error) of the input. Estimation of smoothing parameters is performed by generalized cross-validation.